{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca257a96-9213-4d01-a13b-c7b773d80519",
   "metadata": {},
   "source": [
    "# Replication of Results in Publication\n",
    "Shin, J., King, J., Walker, R., Blackwood, J., Coult, J., Chapman, F., ... & Rea, T. (2025). Real-time cerebral oximetry and outcomes during out-of-hospital resuscitation. Resuscitation, 110761.  \n",
    "https://pmc.ncbi.nlm.nih.gov/articles/PMC12977516/\n",
    "https://www.sciencedirect.com/science/article/pii/S0300957225002734?via%3Dihub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5c3a71b-c3ad-475d-96ac-f574c51eb1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statistics as stats\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(16,8.27)})\n",
    "plt.rcParams.update({'font.size': 22})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "789c593d-546a-4736-9cba-fa47e57b0592",
   "metadata": {},
   "source": [
    "## Load compiled df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e512666a-d2ca-469a-be8a-6fab9e6d60c1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>CASSID</th>\n",
       "      <th>Combo Name</th>\n",
       "      <th>ROSC Time (sec)</th>\n",
       "      <th>ROSC Time index</th>\n",
       "      <th>CPR Time Post-ROSC (sec)</th>\n",
       "      <th>CPR Index Post-ROSC</th>\n",
       "      <th>CPR Post-ROSC</th>\n",
       "      <th>BrainOx First Signal (sec)</th>\n",
       "      <th>ROSC</th>\n",
       "      <th>...</th>\n",
       "      <th>Pre-ROSC BP</th>\n",
       "      <th>Post-ROSC BP</th>\n",
       "      <th>Systolic BP 1</th>\n",
       "      <th>Diastolic BP 1</th>\n",
       "      <th>Systolic BP Last</th>\n",
       "      <th>Diastolic BP Last</th>\n",
       "      <th>BP1 - ROSC (sec)</th>\n",
       "      <th>BPlast - ROSC (sec)</th>\n",
       "      <th>3minStartMedian, min 1, all cases</th>\n",
       "      <th>5minStartMedian, min 1, all cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20009240</td>\n",
       "      <td>2002 - 20009240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>77913.666</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20009282</td>\n",
       "      <td>2005 - 20009282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>25460.067</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20009303</td>\n",
       "      <td>2007 - 20009303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>17368.771</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20009324</td>\n",
       "      <td>2009 - 20009324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>46005.629</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20009346</td>\n",
       "      <td>2012 - 20009346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>36202.023</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>20011565</td>\n",
       "      <td>2179 - 20011565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>66216.110</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>20011610</td>\n",
       "      <td>2182 - 20011610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>66436.645</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>90</td>\n",
       "      <td>20011601</td>\n",
       "      <td>2183 - 20011601</td>\n",
       "      <td>40035.34</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>39703.657</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>154.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>20011627</td>\n",
       "      <td>2185 - 20011627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>70117.406</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>92</td>\n",
       "      <td>20011648</td>\n",
       "      <td>2186 - 20011648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>63334.774</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>No ROSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0.1    CASSID       Combo Name  ROSC Time (sec)  ROSC Time index  \\\n",
       "0              0  20009240  2002 - 20009240              NaN              NaN   \n",
       "1              1  20009282  2005 - 20009282              NaN              NaN   \n",
       "2              2  20009303  2007 - 20009303              NaN              NaN   \n",
       "3              3  20009324  2009 - 20009324              NaN              NaN   \n",
       "4              4  20009346  2012 - 20009346              NaN              NaN   \n",
       "..           ...       ...              ...              ...              ...   \n",
       "88            88  20011565  2179 - 20011565              NaN              NaN   \n",
       "89            89  20011610  2182 - 20011610              NaN              NaN   \n",
       "90            90  20011601  2183 - 20011601         40035.34             25.0   \n",
       "91            91  20011627  2185 - 20011627              NaN              NaN   \n",
       "92            92  20011648  2186 - 20011648              NaN              NaN   \n",
       "\n",
       "    CPR Time Post-ROSC (sec)  CPR Index Post-ROSC  CPR Post-ROSC  \\\n",
       "0                        NaN                  NaN              0   \n",
       "1                        NaN                  NaN              0   \n",
       "2                        NaN                  NaN              0   \n",
       "3                        NaN                  NaN              0   \n",
       "4                        NaN                  NaN              0   \n",
       "..                       ...                  ...            ...   \n",
       "88                       NaN                  NaN              0   \n",
       "89                       NaN                  NaN              0   \n",
       "90                       NaN                  NaN              0   \n",
       "91                       NaN                  NaN              0   \n",
       "92                       NaN                  NaN              0   \n",
       "\n",
       "    BrainOx First Signal (sec)  ROSC  ...  Pre-ROSC BP  Post-ROSC BP  \\\n",
       "0                    77913.666     0  ...      No ROSC       No ROSC   \n",
       "1                    25460.067     0  ...      No ROSC       No ROSC   \n",
       "2                    17368.771     0  ...      No ROSC       No ROSC   \n",
       "3                    46005.629     0  ...      No ROSC       No ROSC   \n",
       "4                    36202.023     0  ...      No ROSC       No ROSC   \n",
       "..                         ...   ...  ...          ...           ...   \n",
       "88                   66216.110     0  ...      No ROSC       No ROSC   \n",
       "89                   66436.645     0  ...      No ROSC       No ROSC   \n",
       "90                   39703.657     1  ...            0             1   \n",
       "91                   70117.406     0  ...      No ROSC       No ROSC   \n",
       "92                   63334.774     0  ...      No ROSC       No ROSC   \n",
       "\n",
       "    Systolic BP 1  Diastolic BP 1  Systolic BP Last  Diastolic BP Last  \\\n",
       "0             NaN             NaN               NaN                NaN   \n",
       "1             NaN             NaN               NaN                NaN   \n",
       "2             NaN             NaN               NaN                NaN   \n",
       "3             NaN             NaN               NaN                NaN   \n",
       "4             NaN             NaN               NaN                NaN   \n",
       "..            ...             ...               ...                ...   \n",
       "88            NaN             NaN               NaN                NaN   \n",
       "89            NaN             NaN               NaN                NaN   \n",
       "90          154.0            79.0             154.0               79.0   \n",
       "91            NaN             NaN               NaN                NaN   \n",
       "92            NaN             NaN               NaN                NaN   \n",
       "\n",
       "    BP1 - ROSC (sec)  BPlast - ROSC (sec)  3minStartMedian, min 1, all cases  \\\n",
       "0                NaN                  NaN                               14.0   \n",
       "1                NaN                  NaN                               82.0   \n",
       "2                NaN                  NaN                               44.0   \n",
       "3                NaN                  NaN                               16.0   \n",
       "4                NaN                  NaN                               38.0   \n",
       "..               ...                  ...                                ...   \n",
       "88               NaN                  NaN                               19.0   \n",
       "89               NaN                  NaN                               17.0   \n",
       "90             547.0                547.0                               62.0   \n",
       "91               NaN                  NaN                               16.0   \n",
       "92               NaN                  NaN                               43.0   \n",
       "\n",
       "    5minStartMedian, min 1, all cases  \n",
       "0                                13.0  \n",
       "1                                76.0  \n",
       "2                                45.0  \n",
       "3                                16.0  \n",
       "4                                38.0  \n",
       "..                                ...  \n",
       "88                               19.0  \n",
       "89                               18.0  \n",
       "90                               65.0  \n",
       "91                               16.0  \n",
       "92                               43.0  \n",
       "\n",
       "[93 rows x 250 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('cerebral_oximetry_dataset.csv')\n",
    "df.drop('Unnamed: 0', inplace=True, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0291a4c1-c7da-4ccd-b7b5-416d667d5940",
   "metadata": {},
   "source": [
    "# 1 - FIGURE 2\n",
    "From https://seaborn.pydata.org/generated/seaborn.boxplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9b2f7cf-aa1f-43a4-9897-e1c883b92fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(16,6)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=1.2)\n",
    "palette = ['b', 'peru']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff6e0cd-dd05-4316-800a-dced255c611d",
   "metadata": {},
   "source": [
    "## All cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09091a14-d31a-427e-9355-99a8e2e0e859",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1 = df[['BOx@0min (c)', 'BOx@1min (c)', 'BOx@2min (c)', 'BOx@3min (c)', 'BOx@4min (c)', 'BOx@5min (c)',\n",
    " 'BOx@6min (c)', 'BOx@7min (c)', 'BOx@8min (c)', 'BOx@9min (c)', 'BOx@10min (c)', 'BOx@11min (c)', 'BOx@12min (c)', 'BOx@13min (c)',\n",
    " 'BOx@14min (c)', 'BOx@15min (c)', 'BOx@16min (c)', 'BOx@17min (c)', 'BOx@18min (c)', 'BOx@19min (c)', 'BOx@20min (c)', 'BOx@21min (c)',\n",
    " 'BOx@22min (c)', 'BOx@23min (c)', 'BOx@24min (c)', 'BOx@25min (c)', 'BOx@26min (c)', 'BOx@27min (c)', 'BOx@28min (c)', 'BOx@29min (c)',\n",
    " 'BOx@30min (c)', 'BOx@31min (c)', 'BOx@32min (c)', 'BOx@33min (c)', 'BOx@34min (c)', 'BOx@35min (c)', 'BOx@36min (c)', 'BOx@37min (c)',\n",
    " 'BOx@38min (c)', 'BOx@39min (c)', 'BOx@40min (c)', 'BOx@41min (c)', 'BOx@42min (c)', 'BOx@43min (c)', 'BOx@44min (c)', 'BOx@45min (c)',\n",
    " 'BOx@46min (c)', 'BOx@47min (c)', 'BOx@48min (c)', 'BOx@49min (c)', 'BOx@50min (c)', 'ROSC']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "577d1ae9-2c12-4b95-ad11-3a3f1552b9b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make df for Seaborn plotting\n",
    "cols = df1.columns[:-1]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, cassyes, cassno = [], [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno = 0, 0, 0\n",
    "    cyes, cno = [], []\n",
    "    for i in range(len(df1)):\n",
    "        if np.isnan(df1[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1['ROSC'][i] == 0:\n",
    "                roscs.append('No subsequent ROSC')\n",
    "                rno += 1\n",
    "                cno.append(df['CASSID'][i])\n",
    "            else:\n",
    "                roscs.append('Subsequent ROSC')\n",
    "                ryes += 1\n",
    "                cyes.append(df['CASSID'][i])\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    cassyes.append(cyes)\n",
    "    cassno.append(cno)\n",
    "    \n",
    "df1_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1_sns['rSO2 (%)'] = vals\n",
    "df1_sns['Outcome'] = roscs\n",
    "\n",
    "df1_sns\n",
    "\n",
    "ns_df1 = pd.DataFrame(df1.columns[:-1], columns=['Time since ROSC (min)'])\n",
    "ns_df1['n'] = ns\n",
    "ns_df1['n - rosc'] = roscyes\n",
    "ns_df1['n - no rosc'] = roscno\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df1_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aac88611-43a2-407c-a02f-372119b39d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time since Call Pickup (min)</th>\n",
       "      <th>rSO2 (%)</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>75.0</td>\n",
       "      <td>No subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>No subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>49</td>\n",
       "      <td>47.0</td>\n",
       "      <td>No subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>49</td>\n",
       "      <td>25.0</td>\n",
       "      <td>No subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>50</td>\n",
       "      <td>45.0</td>\n",
       "      <td>No subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>50</td>\n",
       "      <td>46.0</td>\n",
       "      <td>No subsequent ROSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>50</td>\n",
       "      <td>27.0</td>\n",
       "      <td>No subsequent ROSC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1089 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time since Call Pickup (min)  rSO2 (%)             Outcome\n",
       "0                                7       3.0     Subsequent ROSC\n",
       "1                                8      75.0  No subsequent ROSC\n",
       "2                                8       7.0     Subsequent ROSC\n",
       "3                                9      29.0  No subsequent ROSC\n",
       "4                                9       1.0     Subsequent ROSC\n",
       "...                            ...       ...                 ...\n",
       "1084                            49      47.0  No subsequent ROSC\n",
       "1085                            49      25.0  No subsequent ROSC\n",
       "1086                            50      45.0  No subsequent ROSC\n",
       "1087                            50      46.0  No subsequent ROSC\n",
       "1088                            50      27.0  No subsequent ROSC\n",
       "\n",
       "[1089 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb7c301-c478-4f5e-a4df-70fb2e839fcf",
   "metadata": {},
   "source": [
    "### Drop bars where n < 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f99d3a9-9407-48e4-9417-69f3d69e4abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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4cOECNmzYgPj4eGzevLneJUJSadOmDcaMGYOMjAzMmjULiYmJ8PDwwJEjR6BUKuHo6ChJ57z11ls4efIknn32WfTu3RuAbTvPXL4tCc2Xq/cs5cvZew1l26rzGsq3VeeZy7dl5wk99uTqvYbybdF75rLl7ryAgABs2rQJNTU1KCoqwq5du3Djxg1otVo4OTnJ3nuW8uVmTb6UvScmX+reE5ItZ+8JyZez9yzly9171hx7UvaekHy5es9Stpy9p9fr8dprr+Ghhx7C+PHjG3ydHN0nNFsuTcmXovesyZey98Tky9F9YvKl7j6h2XL1XlOOPSl6T0y+3O/3OKF5FzH8tayhNRUMlwtItb7K3ayiogKTJk1CaWkpVqxYYbOzM4Hbfy0w/PVw2LBhyM3NRWJiIuzt7TF8+HBZMvV6PRYuXIhu3bo1yw/khjg6OmLatGmYNm0avv76a9kmNF1cXIwfjx492uS53r17o3379vj2229RXV3d5DvwCbFjxw44OTkhOjpa9iwASEpKwvr165GZmYlu3boZH3/iiScwcuRILFu2DMnJybLlL1q0CPb29ti5cycOHToEAHB1dcWCBQuQlJTU5Mt3kpKSsHXrVkRFReH11183Pm6rzmso31aE5svVe0Ly5eq9hrJt1Xli/++l7ryG8m3VeWL2X47eayxf7t5rLFvOzmvTpg369Olj/PeoUaMQHR2N8+fPY8OGDbL3nqV8uYnNl7r3xORL3XuWsuXuPWv/76XqPUv5cveeNfsvZe8JyZer94Rky9V76enpOHnyJDIyMhq9Q7kc3Sc0Wy7W5kvVe9bkS9l7QvPl6r6m/v83pfuEZsvVe03Zdyl6T0y+3O/3eFOgu4i7uzvuvfdelJWVmX3+8uXLcHV1tdlC7s3FUPLnzp3DihUr6v01xdaioqLQqlUrWc8OzMzMxMmTJzFt2jSUlpaiuLgYxcXFxsW8y8vLUVxcDJ1OJ9sYGmJY5+bq1auyZbi7u8PV1RUA4OXlVe95Ly8v3Lp1S9Ci/k2Vn5+PoqIiREVF2WTdVp1Oh40bN6Jz584mJQ/89aYjPz9f1jG4uLjgjTfewJEjR5Ceno7t27fj8OHDGDFiBK5du4bOnTtbvW2lUom1a9di0KBBWLlyJRwd//o7mi06r7F8WxCaL1fvWbv/UvReY9m26Dxr912qzrN07MvdeWL2X47eayxf7t6ztO9ydt6d2rRpA4VCgby8PBQXF9v8vd6d+bbWWL4t3u+J2X+p3+/dmW3r93pi9l2O93rmjn1bvteztP9yv9+7M9+W7/fM7bscvVdZWYn33nsPQ4cOhbu7u/GYNvRbVVWV8RiXuvvEZMvB2nypek+q/be298Tky9F9Uu2/Nd0n9riXuveasu9S9J6YfFv0Hs/QvIvY2dkhKCgIx44dQ0lJicmCyTdv3sQPP/yAoKCg/+kbAmk0GkyePBlnz57FihUrjHcnbU61tbXQarWS3fXSnJKSEgBAYmKi2ednz54NAPj888/RsWNH2cZhzvnz5wHcvmOiXOzs7BAcHAy1Wo2ysrJ667iUlpbC0dHRJmvHZmRkAADGjh0rexZw+wdoTU1Ng38Z1+l00Gq1NhmLm5sbQkNDjf/eu3cv9Hq98Q6MYiUnJyM5ORmDBw82O6kgd+dZypeb0Hy5eq8p+9/U3rOULXfnNWXfpeg8Ice+nJ0ndv+l7j1L+XL2nph9l7rzGmK4hPL69evw8/Oz+Xu9uvnNwVy+Ld/vCd1/Od7v1c1ujvd6Qvddrvd6dx77tn6v19j+2+L9Xt18Z2dnm77fa2jfpew9jUaDyspKZGdnIzs7u97zOTk5yMnJwfTp0zFnzhxJu09sttSsyZey96Taf2t7T0y+4eewlN0n1f5b031is6XuvabsuxS9JyZ/3LhxsvceJzTvMtHR0Th27Bg2bdqERYsWGR/fvn07qqqq8PTTTzfj6OSl0WgwadIknD17FklJSYiKirJp/m+//Wb2LydpaWmoqanBww8/LFv2sGHD0L1793qP7969Gzk5OZg7dy66dOlidnxSKS8vr1fm1dXVxlPAFQqFbNnA7ctj1Go1UlNTsXjxYuPjubm5+PXXX/HEE0+YnLYvB41GY/xh+thjj8maZdC2bVt4eHjgv//9LwoKCkyOs4KCApw7d87md3gHgGvXriEpKQmenp4mCzgLlZycDKVSiaFDhxrvoGiOXJ0nNF8uQvPl6j2h+XL0npBsOTtP6L7L1XlC8+XqPLHHvtS9JyRfrt5ryvd9UzvP3PEEAMXFxfjiiy/g5uZm/EVGjt4Tky8HMfly9J6YfKl7T2i2i4uLLL0nZt/l6D0x+XL0njXHvpS9JzTfyclJ8t5r6vd9U3vv/vvvR0pKSr3Hr1y5gtdffx19+vTBuHHjjDf9kLL7xGZLTWy+1L0nNl/q3hObL3X3ic2XsvvEZkvde9Ye+1L1nph8W/yeywlNG8jKyjLeOa6kpAR6vR6rV682Pj9jxgzjx6NHj0ZWVhZSU1NRWVmJ8PBw/PTTT9i2bRvCw8Prrb0gdX5lZSVSU1ONHwPAmTNnjK8PCAgQ9Q0vJnvSpEk4ffo0hg0bhurqanz66acm2/L390dAQIDgbLH5I0aMQGhoKIKCguDj4wONRoP8/Hx8/fXX8PX1teoOoULzu3TpYvYNx5kzZwAAvXr1smp9FbH7Hx4ejsDAQOPd3wyfP2XKFPTo0UPW/OjoaHz22WdIS0vD1atX8dhjj+HChQvYunUr3NzcMH/+fNmyDXbt2oWbN28iJiamyWfHCM23t7dHQkIClixZgkmTJuG5555Dp06dUFRUhPT0dDg6Ohr/cilHPgBkZ2cjMzMTjz76KDw9PVFcXIwdO3bgjz/+wJo1a+Dh4SEqe+vWrVAqlWjXrh369++P3bt3mzzfqlUrDBw4EIA8nScmX+rOE5svR++JyZe694Rmy9V5Yvdd6s4Tky9154nNN5Cy94Tmy9F7YvZd6s4DgA8++ACHDx9Gv3794OfnB+D2HUCzsrJQVVWFd955x/gLixy9JyZfjt4Tky9H74nJl7r3hGbL1Xti913q3hOTL0fvick3kLL3xORL3XtisuXovXvvvbfezxQAxsvc27dvb/K8lN0nNlvq3hObL3Xvic2XuvfE5kvdfdbsv1TdJzZb6t4Tm28gVe+JzZfj99y6OKFpAzt37qy3NsCqVauMH9edWHBwcMC6deuQkpKCPXv2YPfu3fDy8sLEiRMxc+ZMqxa8FZOv0WhMngOA06dP4/Tp0wBu/4VBTNmLyTZkGE5TvtOsWbNEv8EVkx8XF4fDhw8jLS0NFRUVcHZ2RseOHTFjxgxMnDjRqvWsxOTLQUz+yJEjkZ+fj2PHjuH3339Hq1atEBgYiPnz52Pw4MGy59vb22PNmjX48MMPsWvXLhw4cMD4C2hiYqLov7Ba87X/5JNP4OjoaNUvk03JHzduHHx8fJCammp8c9mmTRv07dsX//jHP6z6BUNMfqdOnVBbW4vNmzfj+vXr8PDwwBNPPIHp06dbddnbqVOnANy+jMLcD2lfX1/jDzo5Ok9MvtSdJzZfjt4Tky9174nJloOYfDk6T0y+1J0nNt9Ayt4Tky9174nJlrrzAGDAgAG4fPkycnNzcfXqVeh0Onh7e+PJJ5/EhAkTEBISYnytHL0nJl+O3hOTL0fvicmXuvfEZMtBTL4cvScmX47es+brL2XvicmXuvfEZMvRe2LJ0X1CydF7YsjRe2LI8XtuSyJH9wklR+9ZQ8reE0OO33PrstPr9XqJxkpEREREREREREQkK97lnIiIiIiIiIiIiFoMTmgSERERERERERFRi8EJTSIiIiIiIiIiImoxOKFJRERERERERERELQYnNImIiIiIiIiIiKjF4IQmERERERERERERtRic0CQiIiIiIiIiIqIWgxOaRERERERERERE1GJwQpOIiIiIiIiIiIhaDE5oEhER0f8rKpUK/v7+UKlUzT0USRUXF8Pf3x/z589v7qHc9caPHw9/f3+Tx9RqNfz9/aFUKmXJVCgUUCgUVn9+SzxuKyoqEBERgSVLlsia4+/vj/HjxzdpG3q9HtHR0YiNjZVoVERERCQnx+YeABEREZG17pyUsuTtt9+WaSQkpXPnzmHbtm1Qq9UoLS3FzZs34e7ujh49emDQoEF4+umn4eLi0qxjVKvViIuLM3nMyckJnp6eCA0NxZQpUxASEtJMo7s7vP/++7h58yamT5/e3EOxyM7ODomJiZgxYwb27t2LIUOGNPeQiIiIqBGc0CQiIqIWa9asWfUe++ijj1BZWYm4uDjcd999Js91794dfn5+6NmzJ7y9vW01TJvw8fFBTk4O3NzcmnsoTZKcnIyUlBTcunULDz/8MEaNGoVWrVqhvLwcx48fx6JFi5CWlnbXnKno6+uLUaNGAQCqqqrw7bffYu/evdi3bx+USiUiIyMBAJs3b27GUdrepUuXsH37djzzzDOyf6/l5OTg3nvvbfJ2IiMj0bVrVyQlJWHw4MGws7OTYHREREQkB05oEhERUYuVkJBQ77HMzExUVlZiwoQJ8PPzM/t5LX3SzxwnJyd06dKluYfRJGvWrIFSqUS7du2watUq9OzZs95rDh48iPXr1zfD6Mzz9fWtdxy+//77SElJwdtvv22c0OzQoUNzDK/ZbN++HTqdzjjZKycpj/uRI0dixYoVOHLkCPr06SPZdomIiEhaXEOTiIiI/l9paC1CwxqHf/zxB5YuXYr+/fsjJCQE0dHR2L9/PwBAq9UiOTkZUVFRCA4OxsCBA7F169YGsw4dOoQXXngBjz76KIKCgjBw4EAsW7YM169fFzzeyspKJCcnY/jw4QgNDUVoaCgUCgUSExPx/fffG1/X0Bqa8+fPh7+/P4qLi5Geno6nnnoKwcHB6NOnDxYtWtTgWMrKyvCvf/3LuK8RERGIiYlBSkqK2dcuWbIEkZGRCAoKwqOPPorp06fju+++E7yfxcXFSElJgZOTE9atW2d2MhMA+vXrV29CU6VSISEhAZGRkQgJCUFYWBiee+45ZGVlCc6X0rhx4wAAFy9exNWrVwE0voZmTk4OJkyYgIiICAQHB0OhUGDu3Lk4deqUxSyNRoPY2FgEBATggw8+AAAolUr4+/tDrVbXe72l4+TixYvYtGkThgwZguDgYPTr1w9Lly7F77//Lnj/9Xo9du7cCT8/P7OX3dfN2rJlC4YPH46QkBAoFAqsXbsWer0eALB7926MGTMGPXv2RO/evfHmm2/i5s2b9bZnbg3Nul+DvXv3IiYmBj179kRERARefPFFlJWVmR378OHDAQA7duwQvL9ERERkezxDk4iIiOhPWq0WkydPRkVFBSIjI6HVapGdnY2EhARs3LgRH330EX744Qf069cPzs7OyM3NxZIlS+Dh4YFhw4aZbCs5ORlKpRLu7u548skn4enpibNnz2Ljxo04ePAg0tPTLZ4pqtfrER8fj4KCAoSGhuKZZ56Bg4MDysrKkJ+fj4KCAgQFBQnat+XLlyMvLw8DBgzA448/DrVajU8++QRFRUXYsmWLyWtPnTqF+Ph4401dBg0ahOrqavzyyy9ITk7GzJkzja89ffo0Jk+eDI1Gg759+yIqKgrXrl3D/v37ERsbi5SUFPTv39/i+FQqFbRaLYYPH45u3bo1+lpnZ2eTfy9evBhdu3bFI488Ai8vL1y7dg1fffUVXnnlFRQWFmLu3LmCvka2ptfrsWDBAmRmZsLDwwODBg2Cp6cnSktLoVar8eCDDyI4OLjBz7906RLi4+Nx4cIFLFu2DNHR0U0e09KlS3H8+HEMHToUbm5uyMvLw0cffYTjx48jLS1N0NqlZ8+exW+//Yannnqq0de9++67yM/Px4ABA9CnTx8cOHAASUlJ0Ol0cHV1xapVqzBw4EA88sgjOHz4MLZs2QKdToc33nhD8P5s27YNBw4cgEKhwCOPPILvvvsOe/bswZkzZ/DZZ5/VO5bat28PHx8fHD58GHq9npedExER3aU4oUlERET0p19//RWBgYFITU01TnRER0dj3LhxSEhIQMeOHZGdnW1cm3Py5MkYMmQI1q1bZzKhefToUSiVSoSFhWHdunUmE5cqlQoLFizA+++/j4ULFzY6np9++gkFBQWIjIzE6tWrTZ67desWKisrBe/bd999h88++wzt27cHAOh0OkyYMAHHjh3Dt99+azwjsqamBrNnz0ZFRQXee+89jBgxwmQ7paWlxo91Oh1efPFFVFdXY8uWLQgPDzc+d/nyZcTExODVV1/FgQMHLE6EHT9+HADQu3dvwftkkJ2dXe+S7pqaGkyZMgUbNmxAbGws/va3v4nerrXS0tIAAH5+fvD09GzwdRkZGcjMzERISAg2btxocpzU1tbiypUrDX7ujz/+iPj4eFRXV2PdunWSXR594sQJZGVlwdfXFwAwb948zJ49G59//jnWr19vMpnd2DYAWJxsP336NHbt2gUfHx8At5eQGDRoENavXw8XFxeoVCrj5eQ1NTUYPXo0du7cicTERNx///2C9ufQoUPYsWOHyQ3E5s2bh+zsbOzfv7/eHyIAIDg4GPv378cvv/yChx56SFAOERER2RYvOSciIiKqY+HChSZnbYWHh8PPzw+VlZV46aWXTG405Ofnh7CwMJw9exa1tbXGx1NTUwEAb775Zr2zMEePHo3u3bsjOzvb4lgMZ4eZu+GJvb092rRpI3i/ZsyYYZzMBABHR0eMHj0aAEwubf7yyy9RUlIChUJRbzITANq1a2f8+KuvvsKFCxfw/PPPm0xmArdvUhQfH4/y8nIcOXLE4vjKy8uNnyeWufUpnZ2d8fzzz0On0+Ho0aOitylUSUkJlEollEol3n33XTz//PNQKpWwt7evd1n3nQxnxr7xxhv1jhMHB4cGb6Zz+PBhxMbGAgC2bt0q6VqPcXFxxslM4PZx9s9//hP29vbYuXOnoG0YJr0tTTrOmDHD5P/7vvvug0KhQHV1NWJjY03WxnR2dsaQIUOg1Wpx7tw5UftTdzITAMaOHQsADV7S37ZtW5P9ICIiorsPz9AkIiIi+tN9992HBx54oN7j3t7eKC4uNnvGmbe3N2pra1FeXm6cnCkoKICTkxP27NljNker1eLq1au4du0aPDw8GhxP165d0aNHD2RnZ6O0tBQKhQJhYWEICgqqd6msJeYuXTZMTmo0GuNjBQUFAG6vVWmJ4bWGSb07FRUVAQAKCwvx5JNPNrotw7qJ1lzie+nSJXz44Yc4cuQISktLcePGDZPnL1++LHqbQpWUlCA5ORnA7UliDw8PREVFYdKkSQgLC2vw86qqqnD27Fm0bdsWPXr0EJyXm5uL//znP+jQoQPWr19vMkkthYiIiHqPPfDAA2jXrh1KSkpw/fp1k0l9cyoqKgDA4usa+n4CgMDAwHrPGb6/Glr/UmiGueO+Lnd3dwDAtWvXBOcQERGRbXFCk4iIiOhPDa1p6ejo2ODzhue0Wq3xsYqKCuh0OuNEV0OqqqoandB0cHDA5s2bkZKSgtzcXCxfvhwA0Lp1a4waNQpz586Fq6tr4zv1p9atW5vdPnD78nUDw2XsQs6UNExc7d27t9HXVVVVWdyWt7c3CgsLRU1WAbdvvBMTE4Pr168jPDwcffv2RevWreHg4ICSkhJkZmaipqZG1DbFiIiIMJ6RK4aYr3NdBQUF0Gq16Nmzp8nZslJp6KzKtm3boqSkBJWVlRYnKg3LC1j6ups7Jhv7XjMcrzqdrtHt1tXYduoe93UZJsTvuecewTlERERkW5zQJCIiIpJY69atodfrkZ+f3+RttWnTBq+++ipeffVVnD9/Hvn5+di+fTtSU1NRWVmJZcuWSTDivxgmgISc1Wh47erVqxEZGdmk3F69euHo0aM4evSo8ZJgITZt2oSKigq8/fbbxkvoDbKzs5GZmdmkcclFzNe5rjlz5uCrr76CSqUCALz11luwtzddRcpwlmvdZRAMLK27euXKFXTu3Lne44YlASzdyAr4a1LUMOHd0hjGLXSdTiIiIrI9rqFJREREJLGHH34YGo0GP//8s6Tb7dixI8aOHYstW7bA1dUV+/btk3T7wO2xA0BeXp7F1xpuJGS4oU9TjB49Gk5OTsjNzcUvv/zS6Gvrnvl3/vx5AEBUVFS910kxoSwXV1dXdOvWDeXl5Thz5ozgz3N2doZSqcTgwYOhUqnw8ssv15u4NKytam4NyO+//77R7Zv7ml28eBGlpaXw9fW1eHYmAOOalYWFhRZfezcqLCyEvb09unXr1txDISIiogZwQpOIiIhIYhMnTgQAvPbaa2bPwKuqqjKuP9mYixcvmp0U1Wg00Gq1Fu8cbo0BAwbA19cX+/fvR05OTr3n6+5PZGQkOnTogG3btuHrr782u72TJ0+iurraYq6fnx9mzZoFrVaLqVOnNnjDloMHDyI+Pt74b8MNbNRqtcnrDHe3vpuNHz8eALB48WL8/vvvJs/V1tbi119/Nft5Tk5OSEpKwogRI5CdnY05c+aYLHlgmGhWqVQml2eXlpYiJSWl0TF9/PHHKCkpMf771q1bePfdd3Hr1q16Z8A2JDw8HA4ODoKO8btNTU0Nzpw5g+7duwuavCUiIqLmwUvOiYiIiCTWu3dvzJs3DytXrsTgwYPRr18/+Pn5oaqqCpcuXcKxY8cQFhaGDRs2NLqdn376CTNnzkRgYCC6desGb29vXL16FV988QW0Wi1eeOEFycfu7OyMVatWYcqUKZgzZw7S09MREhKCGzduoLCwEEePHsUPP/wA4PbEmlKpRHx8PKZOnYrQ0FB0794d99xzD8rKynDq1ClcvHgReXl5Zu/Ufqfp06dDp9MhJSUFMTExCA0NRVBQEFq1aoXy8nIcP34cRUVFJjd6iY2NhUqlwosvvoioqCj4+Pjg559/xqFDhzB06FCzk7J3i7Fjx+Kbb75BVlYWBg0ahMjISHh6euLy5ctQq9UYM2YMEhISzH6ug4MDli9fDmdnZ6hUKmi1WqxatQrOzs4ICQlBREQE8vPzMXbsWDz22GMoLy/Hl19+ib59+zZ69+5evXph5MiRGDp0KNzc3JCXl4cff/wRgYGBgo83Nzc39O7dG2q1GhqNxnjGaEugVquh1WoxePDg5h4KERERNYITmkREREQymDp1KsLCwpCamopvvvkGBw4cQOvWreHj44NnnnkGI0aMsLiNoKAgTJs2Dfn5+Th06BA0Gg08PT0RGBiI8ePHo3///rKMPTg4GFlZWVi3bh0OHjyIEydOoFWrVujQoUO9CbaAgAB8+umn2LRpk3FtR3t7e3h5eaFHjx5ISEho9MZHd5o1axaGDh2Kbdu2Qa1WQ6VSoaamBu7u7ggICEB8fDyio6NN8j/++GP8+9//xsGDB6HT6RAQEIDk5GS4ubnd1ROadnZ2WLZsGR5//HFkZGRgz549qKmpgZeXF3r16gWFQtHo59vb22Pp0qVwcXFBWloaZsyYgZSUFLi4uGD16tVYvnw59u3bh9TUVHTq1Akvv/wyHn/8cezZs6fBbS5YsAD79u1DRkYGSkpK4O7ujri4OMyePVvUGcF///vfkZeXh927dyM2Nlbw5zW3rKwsODk5ISYmprmHQkRERI2w0+v1+uYeBBERERERNZ/58+cjMzMTX3zxBfz8/Jq8vdraWjz11FNwcnJCVlaW8UZFd7MrV65AoVBgxIgReOutt5p7OERERNQIrqFJRERERESScnBwwCuvvIIff/wRn3/+eXMPR5C1a9fC3t4es2fPbu6hEBERkQWc0CQiIiIiIsn1798fCxcuxM2bN5t7KBbp9Xp4eXlh+fLl8Pb2bu7hEBERkQVcQ5OIiIiIiGQRFxfX3EMQxM7ODlOnTm3uYRAREZFAXEOTiIiIiIiIiIiIWgxeck5EREREREREREQtBic0iYiIiIiIiIiIqMXghCYRERERERERERG1GJzQJCIiIiIiIiIiohaDE5pERERERERERETUYnBCk4iIiIiIiIiIiFoMTmgSERERERERERFRi8EJTSIiIiIiIiIiImox/g8ThAdJw8laOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop the bars where n < 4\n",
    "roscdelis = []  # rosc indices/minute since Call Pickup where n < 4\n",
    "noroscdelis = []  # No-rosc indices/minute since Call Pickup where n < 4\n",
    "for i in range(len(ns_df1)):\n",
    "    if ns_df1['n - rosc'][i] < 4:\n",
    "        roscdelis.append(i)\n",
    "    if ns_df1['n - no rosc'][i] < 4:\n",
    "        noroscdelis.append(i)\n",
    "# indices to delete in df1w_sns\n",
    "inds = []\n",
    "for i in range(len(df1_sns)):\n",
    "    if df1_sns['Time since Call Pickup (min)'][i] in roscdelis and df1_sns['Outcome'][i] == 'Subsequent ROSC':\n",
    "        inds.append(i)\n",
    "    if df1_sns['Time since Call Pickup (min)'][i] in noroscdelis and df1_sns['Outcome'][i] == 'No subsequent ROSC':\n",
    "        inds.append(i)\n",
    "\n",
    "df14_sns = df1_sns.copy()\n",
    "df14_sns.drop(inds, inplace=True)\n",
    "df14_sns.reset_index(inplace=True, drop=True)\n",
    "df14_sns\n",
    "\n",
    "# Get ns_df1w4 from df1w4_sns\n",
    "ns = []\n",
    "nrosc = []\n",
    "nnorosc = []\n",
    "for min in range(df14_sns['Time since Call Pickup (min)'][0], df14_sns['Time since Call Pickup (min)'][len(df14_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    rosc = 0\n",
    "    norosc = 0\n",
    "    for index, row in df14_sns.iterrows():\n",
    "        if df14_sns['Time since Call Pickup (min)'][index] == min:\n",
    "            n += 1\n",
    "            if df14_sns['Outcome'][index] == 'Subsequent ROSC':\n",
    "                rosc += 1\n",
    "            else:\n",
    "                norosc += 1\n",
    "        elif df14_sns['Time since Call Pickup (min)'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    nrosc.append(rosc)\n",
    "    nnorosc.append(norosc)\n",
    "ns_df14 = pd.DataFrame(list(range(df14_sns['Time since Call Pickup (min)'][0], df14_sns['Time since Call Pickup (min)'][len(df14_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df14['n'] = ns\n",
    "ns_df14['n - rosc'] = nrosc\n",
    "ns_df14['n - no rosc'] = nnorosc\n",
    "ns_df14\n",
    "\n",
    "#Plot the boxplot\n",
    "# plottie = sns.boxplot()\n",
    "palette = ['lightgray', 'darkturquoise']\n",
    "plottie = sns.boxplot(data=df14_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"Outcome\", palette=palette, linewidth=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b461dc63-bf1a-4a2e-80ca-9ce8e12b4201",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plottie.figure.savefig('fig1.png',dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e757bff-a7b4-4b49-b41b-b3efe1e41d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(12,4)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=.8)\n",
    "sns.boxplot()\n",
    "palette = ['tomato', 'lightgray']\n",
    "ax = sns.boxplot(data=df14_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue_order = ['Subsequent ROSC', 'No subsequent ROSC'], hue=\"Outcome\", whis=0, showfliers=False, palette=palette, linewidth=0.6)\n",
    "ax.set(ylim=(0, 100))\n",
    "ax.figure.savefig('figures/fig2.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bcf86498-27d6-4d24-b64c-8b8f53d58c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time since Call Pickup (min)</th>\n",
       "      <th>n</th>\n",
       "      <th>n - rosc</th>\n",
       "      <th>n - no rosc</th>\n",
       "    </tr>\n",
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       "      <td>22</td>\n",
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       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>49</td>\n",
       "      <td>22</td>\n",
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       "      <td>33</td>\n",
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       "      <th>14</th>\n",
       "      <td>24</td>\n",
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       "      <td>35</td>\n",
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       "      <th>15</th>\n",
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       "      <th>22</th>\n",
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       "      <td>24</td>\n",
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       "      <td>17</td>\n",
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       "      <td>17</td>\n",
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       "      <th>26</th>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>37</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
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       "      <td>14</td>\n",
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       "      <td>12</td>\n",
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       "      <th>32</th>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
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       "      <td>9</td>\n",
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       "      <th>35</th>\n",
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       "      <td>7</td>\n",
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       "      <th>36</th>\n",
       "      <td>46</td>\n",
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       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
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       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Time since Call Pickup (min)   n  n - rosc  n - no rosc\n",
       "0                             10   4         0            4\n",
       "1                             11   5         0            5\n",
       "2                             12  13         5            8\n",
       "3                             13  16         7            9\n",
       "4                             14  21         8           13\n",
       "5                             15  27        11           16\n",
       "6                             16  30        13           17\n",
       "7                             17  37        15           22\n",
       "8                             18  46        20           26\n",
       "9                             19  49        22           27\n",
       "10                            20  54        24           30\n",
       "11                            21  59        26           33\n",
       "12                            22  57        25           32\n",
       "13                            23  55        22           33\n",
       "14                            24  52        17           35\n",
       "15                            25  45        12           33\n",
       "16                            26  41        11           30\n",
       "17                            27  39        10           29\n",
       "18                            28  39        10           29\n",
       "19                            29  33         8           25\n",
       "20                            30  33         8           25\n",
       "21                            31  34         9           25\n",
       "22                            32  32         8           24\n",
       "23                            33  28         7           21\n",
       "24                            34  23         6           17\n",
       "25                            35  23         6           17\n",
       "26                            36  21         5           16\n",
       "27                            37  22         6           16\n",
       "28                            38  19         4           15\n",
       "29                            39  15         0           15\n",
       "30                            40  14         0           14\n",
       "31                            41  12         0           12\n",
       "32                            42  11         0           11\n",
       "33                            43  10         0           10\n",
       "34                            44   9         0            9\n",
       "35                            45   7         0            7\n",
       "36                            46   6         0            6\n",
       "37                            47   6         0            6\n",
       "38                            48   6         0            6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ns_df14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b43a2934-e906-479e-a1b5-21549bf4dcb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 100.0)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+fBUWFqpWrVq6/vrrNWvWLL3zzjsqLi7WgQMHdMstt6iwsFBXXXWVJOmZZ57RRx99pPz8fJWVlendd9/V9u3b1blzZ7lcLk2bNk1z587Viy++qF9++UWGYeirr77SDTfcENJnDwS3gwMAAACAOJCQkKCmTZtqxIgRli6nadOmSkgI/hrxH//4Ry1cuNAzjlySZsyYoXvuuUf9+/dXQkKCLr30Uk2YMCHgmH379tXDDz+sF154QVdddZXGjh2revXq6ZFHHtENN9yglJQUnX/++VqyZImnDD41NVUPPvigdu/eLZfLpWbNmmnmzJk6++yzJUkXX3yxateurblz5+r+++9XcnKyWrZsael6dRmGYVgW3SE2b94sSerYsWOFx/Pz89WlSxdJJ2cgTEtLi3jbAAAAACBYhYWF2rlzp1q1aqWUlBTP46WlpRVmSbdCQkKCZfeJdyJf34WvPNQbrrgDAAAAQJxITEwkqXYgxrgDAAAAAGBjJO4AAAAAANgYiTsAAAAAADZG4g4AAAAAgI2RuAMAAAAAYGMk7gAAAAAA2BiJOwAAAAAANsZ93AEAAAAgTpSWlqqsrMzSZSQkJHCveJORuAMAAABAHCgtLVWvXufr0KH/Wrqc+vUbae3a9wNK3kePHq0vvvhCNWrUUHJysjp06KAZM2aoVatWkqQff/xRDz30kN577z0VFBTojDPO0Lhx4zRkyBBPjA0bNujvf/+7tm3bJklq0aKFpkyZot69e0uSjh8/rscff1xvvfWWDh48qAYNGuh3v/udJk2apNNPP92CNWA+EncAAAAAiANlZWU6dOi/+vHH/pJcFi3FkPSWysrKAr7qPn36dGVnZ6ugoED/7//9P02fPl1LlizRkSNHlJ2drczMTC1btkwNGjTQhx9+qGnTpunw4cO68sordezYMU2YMEHTp0/XoEGDdOLECW3cuNGz7OLiYo0dO1YpKSmaO3euWrdurWPHjmn58uX66KOPNGzYMIvWg7lI3AEAAAAgrrhk3XRnoZfhp6am6tJLL9UNN9wgSXrmmWeUnJysBx54wJOI9+nTR7fddpvuuOMO/eEPf9DOnTt14sQJXX755ZKkGjVqqEePHp6Yr7/+uvbs2aM1a9aoVq1akqS6detqzJgxIbczGpicDgAAAAAQdceOHdOKFSvUokULSdK6det0ySWXVLlyP2DAABUVFSk3N1etWrVScnKypk6dqnfeeUcHDx6s8Nq1a9fqvPPO8yTtTkXiDgAAAACImvvvv19nn322zj77bG3cuFEPPvigJOnQoUNq0qRJldcnJSWpXr16OnTokGrVqqWcnBwlJydr5syZ6tWrl8aMGaPvvvtOknT48GGvMZyGxB0AAAAAEDW33nqrPv/8c7399ttKTEzU7t27JUn169fXjz/+WOX1JSUlOnz4sOrXry9JOuuss3TffffpnXfe0Zo1a5SUlKSbb75ZklSvXj2vMZyGxB0AAAAAEHXNmzfX9OnTNXPmTBUWFuqcc87R6tWrq9y+7q233lLNmjXVpUuXKjFOP/10jRo1St98840kqVevXvrwww917NixiHwGq9g6cd+7d6+uueYade/eXT179tT06dOVn58vSdq2bZuGDx+uzp07a+DAgVq/fn2UWwsAAAAATmDo5CRyVvxnhNWy3r17q3HjxnrhhRd01VVXqaCgQLfccosOHDig4uJivfvuu7r77rt1/fXXq1atWtqxY4cWLFig/fv3yzAM/fzzz1q6dKknqR88eLCaNWum6667Tnl5eSotLdXRo0f1/PPPa+nSpWG1NZJsPav8HXfcoQYNGuj9999XQUGBJk2apCeeeEJTpkzRxIkTdcUVV2jx4sVauXKlJk2apDVr1qhu3brRbjZQLcMwVFBQ4Pm3JLlcLqWmpsrlsurWHAAAAIhnCQkJql+/kaS3LF1O/fqNlJAQ+jXi8ePH67777tOf/vQnLVmyRH//+981ePBgFRYWqmXLlrrllls8s8jXqlVLX375pZ577jkdPXpU6enp+t3vfqc777xTkpScnKynn35ac+bM0YQJE3Tw4EHVr19fvXr10qRJk8z4uBHhMtxZgw1dfPHFuu2229S7d29J0sKFC/XJJ59ozJgxuvnmm/XBBx94NoihQ4cqOzs7pPvwbd68WYZhqHXr1hUeLygoUM+ePSWdnNEwNTU1zE8EnEzUx44dq40bN1Z5LjMzUwsXLiR5BwAAQFiKioq0f/9+nXHGGUpJSfE8XlpaWqX03GwJCQkB38M9HhQWFmrXrl067bTTVLNmTc/j27dvl8vlUseOHauNYesr7ldeeaVWrFihbt26qaCgQKtXr9agQYOUl5enjIyMCmdx2rVrp7y8vJCXVVJSoi1btlR4rLCw0PPvrVu3VtjggVCVv9peWX5+vrZs2ULiDgAAgLDVqFFDRUVFEV9uaWmpSkpKIr5cuyoqKtKJEyf07bffVnkuOTk5oBi2Ttx79OihV155RWeffbbKysp04YUXavjw4XryySdVu3btCq+tU6eOjh49GvKykpKSvF5xd2vXrh1X3GGaF154QYWFhSooKFDfvn0lSf/+979Vv359knbAxgzDUGFhYYUhLpKUkpJC3wUA2Ir7invNmjW5AGkDNWrUUIsWLapccQ/4/VY0ygylpaW6+uqr9Yc//EFLlixRSUmJ7rnnHt10003KzMysMiugezxDqFwul9LS0nw+n5qa6vd5IFjp6emeyRYlqUGDBmxjgI0ZhqHs7Gzl5uZWeS4rK0s5OTkk7wAA20hISPCUrFO2Hl2JiYlKSEhQampqhZMowRw32HZW+SNHjuiHH37QqFGjVLNmTdWqVUvZ2dl677331KZNG23btq3C2IwtW7aoTZs2UWwxACDWkZgDAIBosG3i3qBBAzVv3lw5OTkqLi5Wfn6+XnrpJbVt21bdu3dXcnKyFi5cqOLiYq1YsUJ79+7VRRddFO1mAwCiyDAM5efn6/jx4zp+/Ljy8/OVn58vM+ZhdblcysnJ0bp16zyPrVu3Trm5uVxtBwDYltUT0aF6ZnwHti2Vl6THH39c9913n5577jlJJ2fcfuCBB5SUlKS5c+dqxowZmj17tpo3b645c+aoXr160W0wACBqIlHK7r5toxvDqAAAdpWcnKyEhATt379fjRs3VnJyMieZI8wwDBUXF+unn35SQkJCwBPReWPrxL1du3Z69tlnvT7Xtm1bLV26NMItAgDYGQckAACclJCQoFatWun777/X/v37o92cuJaWlqYWLVqEdW97WyfuAAAEyl3KfvDgQfXs2VPSyVL21NRUpaamktQDAOJOcnKyWrRooRMnTqi0tDTazYlLiYmJqlGjRtjHISTuAICYQSk7AAAVuVwuJSUlKSkpKdpNQRhsOzkdAAAAAAAgcQcAAAAAwNZI3AEAAAAAsDESdwAAAAAAbIzJ6QAAQNwzDEMFBQUyDEPSr7cW5I4EAAA7IHEHAABxzTAMZWdnKzc3t8pzWVlZysnJIXkHAEQVpfIAACDukZgDAOyMK+4AgIhxlyO7/y39eu91EidEi8vlUk5Ojg4ePKiePXtKktatW6fU1FTHbJveSv2d0nYAQPVI3AEAEUE5MuzMnei6paamKi0tLYotCpyvvkW/AoDYQak8ACBiSCAAa9C3ACC2ccUdiEFWlyMz+3J0ObUk1l2OXFBQoIKCggolyQ0aNLB9+wG78lXqT78CgNhB4g7EGKvLkSl3ji6nl8S6XK4q5cdOOOkA2J23Un/6FQDEDkrlgRhk9cEaB4PRxfoHAACIL1xxB2KM1eXIzL4cXZTEAgAAxB8SdyAGWV2OzOzL0UVJLAAAQHyhVB5A3CHJBQAAgJNwxR1AXKHUHAAAAE5D4g744ORx0E5n9e3mKDX3zepbCQIAgsMtWAFIJO6AV7EwDtqpuN1c9LDuAcBe+F0G4MYYd8AHdoTRw7qPHtY9ANgLv8sAJK64A17ZeRx0rJfwx8Lt5pzK6lsJAkAssnKIEftEAG4k7oAPdhwHHS8l/E6+3ZzTWX0rQQCIJZEoZWefCECiVB5wHBIoAADsg/0ygEjgijsswyyo5rNzCT8AWMnpdzxgnxibGGIEIFJI3GEJZkG1jh1L+AHASk7fpzi9/fCPIUYAIoFSeViGHRYAwCxO36c4vf0AgOjiijsswSyoAACzOL0cmX0iACBcJO6wDLOgAgDM4vRyZPaJAIBwUCoPAAAAAICNccUdAEzmbfZoJ10ZBIBI43cTAPwjcQcAE/maPZqZowHAO343AaB6lMoDgMk4yASA4PC7CQD+ccUdAEzka/ZoJ8x8DQC+WFnKbvXvprvt7n+7l0kpPgAnIXEHAJN5mz2ag0MAThWJUnarfjd9tV2iFB+As1AqDwAAAL+cnNw6ue0A4MYVdwAAAPjk5CFA7rYXFBSooKDAce0HADcSdwAAAPjl5CFALpdLaWlpFR5zUvsBQKJUHgAAAAAAW+OKOwAANmHlzN1Wx2fmbthJ+e3RrfzflZ9jOwVgdyTuAADYgNUzd1sZn5m7YTcFBQXq0qWLz+fdY93dcnNzq5TTA4CdUCoPAIBNWJ3cWhmfxBwAAOtwxR0AABuweuZuK+Mzczfs7Kef+sowEv/3l/G//7vkcpWqceN/R6tZABAUEnfgf4IdDycxJg6AuayeudvK+MzcDbs6mbRXPeQ1jKqvBQC7InEH/ifY8XASY+IAAAAAWI8x7gAAAAAA2BhX3AEvfI2Hk8SYOAAAAAARReIOeOFrPNzJ5yLbFgAAYC/cJx5ApJG4AxHCTh4AgNjAfeIBRBqJOxAh7OQBAAAAhML2k9O99dZbGjhwoDIzM3XhhRdq9erVkqRt27Zp+PDh6ty5swYOHKj169dHuaUAAACINz/91Fc//njx//676H//Xayffuob7aYBiCG2vuL+0Ucf6d5779VDDz2krKwsHTp0SPn5+SopKdHEiRN1xRVXaPHixVq5cqUmTZqkNWvWqG7dutFuNlAtX5PfhTPxHaX4ABCcyr+b/n4zJX434R33iQcQCbZO3GfPnq3Jkyera9eukqSGDRuqYcOGWrt2rQoLCzV+/HglJCRoyJAhevbZZ7V69WoNGzYspGUZhqH8/PwKj1W3A0f1nLQOQ2lfMO8p/9pAdvL+YntbrwUFBV7vNe9W+bl169YpNTW1umYH1QYzEZ/4doxN/NiK7+9309vj/G46J36w77fb6/293+7HUwACZxhGwCeEbZu4l5aWavPmzbrgggt00UUXqaCgQL169dL06dOVl5enjIwMJST8Wunfrl075eXlhby8kpISbdmypcJjhYWFnn9v3bpVKSkpIcePV05ah+XbGqhgPlOw8f3F9rZezYwfCKu/W+IT346xiR9b8fndjN34Vn+3sbbtAIie5OTkgF5n28T9v//9r0pKSrRy5UotWrRIaWlpmjp1qu699161aNFCtWvXrvD6OnXq6OjRoyEvLykpSa1bt67wWPkzmu3atQvrLHu8ctI6DOUMdjCfKdj4/mJ7W6/lHwukFD/c78Pq75b4xLdjbOLHVnzvv5u//mZK4nfTofHN3OdGI76/5dn9eApA4LZv3x7wa22buLt/kEaOHKlTTjlFknTttddq8uTJuvbaa3Xs2LEKrz969KjS09NDXp7L5fI7g3dqaiozfIcpFtehlZ8p0NjeXhdIKb6Zbbf6uyU+8e0Ym/ixFZ/fzfiKH+nlOWnbARA5wcybEtas8ocPH9aBAwdUVFQUThiv6tSpo1NPPdXrh2nTpo22bdumsrIyz2NbtmxRmzZtTG8HAAAAAADRFNQV9/z8fP3rX//SqlWr9MUXX1SYzK1ly5bq2bOn/vCHP+i3v/2tKY374x//qOeff169e/dWamqq5s+frz59+qh79+5KTk7WwoULNWbMGL311lvau3evLrroIlOWCwAAAACAXQScuM+fP18LFizQmWeeqd69e2vs2LFq3LixUlJSdOTIEW3fvl0bNmzQ1Vdfrfbt2+u2227TWWedFVbjrr32Wh0+fFiXXnqpEhMTdcEFF2j69OlKSkrS3LlzNWPGDM2ePVvNmzfXnDlzVK9evbCWBwAwH7cqBAAACE/Aift3332npUuXqkWLFl6f79Spk4YOHaq77rpLb7zxhr7++uuwE/caNWpoxowZmjFjRpXn2rZtq6VLl4YVHwBgvYKCAnXp0sXn85Vvu5Wbm8v4TUjipI8/wa4bKb7WDwDEmoAT91mzZgX0usTERA0ZMiTkBgGwp8oHiRwgArAaJ318C3bdSPG1fgAg1oQ9q/zPP/+sTZs2qbS0VB06dPDMAA8gtvg7SOQAEYEK5FaFAAAAqCisxH3ZsmV65JFH1KVLF5WUlOi2227T9ddfr5EjR5rVvogqP9mexBVFADBbILfcArzhpI9vvtaNJNYPAMSIoBL3/fv367TTTvP8/eSTT+q1115TgwYNJJ28wjZp0iRHJu6GYVByBgTo14NEDhABRAYnfXzztW5OPhfZtgAArBHUfdzHjh2refPmqaSkRJLUsGFDffjhhyouLtaxY8e0du1aNW7c2JKGwnyGYSg/P1/Hjx/X8ePHlZ+fr/z8fBns5VGNXw8Sk/73Xw1JNcpd8QEAANHkPs6rfKzHcR7gTEFdcX/11Vf12GOP6fLLL9f06dN1//336+6779bdd9+tsrIyde7cWY888ohVbY2YhQsXqmbNmpLk+XFzl8QXFRVp3LhxUWubWQzDUHZ2tnJzc6s8l5WVpZycHIYBIKKY/A4AAHNwnAfEnqAS9/T0dN1666365ptvNHPmTDVq1Eh33nmnmjZtalX7oqJmzZpKSUmJdjMsxw827ITJ7wAAMA/HeUBsCWlyurZt2+r555/XK6+8opEjR+pPf/qTxo4dq8REymSdwuVyKScnRwcPHvQkRevWrVNqaipXMgEAACLAXW1WvsLTjOMw93FeQUGBCgoKKhzrNWjQgOM8wIGCStwPHTqkefPmaefOnWrTpo0mTJigfv366eGHH9Zll12mGTNmqEePHla1FSZz7xzcUlNTuYIJWzB78rvKZfgSpfgAgOjyVc5uVim7y+WqclzHvg1wrqAS97/+9a+qV6+eRo4cqbVr1+q2227T7Nmzddddd2nTpk2aOXOmWrRooYcfftiq9gKIA2bPHu2vDF+iFB8AEB0k0QACFVTivmHDBr3++utq3ry5zjvvPPXt29fzXKdOnbR06VItWbLE9EYCQDyjYsD5yn+HZpfERoK3cl6J7aw6Zky66WvbYWZw5/M1bJFS9pP43fHPqmEWsK+gEvfMzEzNnj1bQ4YM0dq1a/Xb3/62wvMul0sjRowwtYEAYKZfy/Alp5TiUzHgbE6f3dnp7Y+mcCfd9LfuMzMzTWsnosfbsEX3iZl4vtMKvzv+WT3MAvYUVOL+0EMP6Z///Keee+45nXXWWZo1a5ZV7QIAS/gqwz/5XGgxSaxRHacfRDm9/U7Guo9P3GmFbb86rJ/4E1Ti3qBBA02fPt2qtgBBoUQI8ciKigGnsLLPW1nKHonZna0sKY2Fu5D42nbMjO3+t6/4oUy66W/bSUlJUVZWlmntt6ocORrxzfpuIxHfySLxu2nl746TS/EZZhGfAk7cP//8c5199tkBvfb48ePat2+fMjIyQm4Y4A8lQrArqxNrKyoGnMDKPh+JkkwrZ3eOVPu93YXEMAzl5+dXeK2/kt5oHBD723YWLFhgSWxv8UOddNPXtmMGq7edaMU347uNRPxAmX2nFTNE83fHDLFQiu9rmAViV8CJ+1133aU6depoyJAh6t27t5o0aVLh+ZKSEm3evFkrV67UypUrNX36dBJ3WIofJ9hRvCbWkWBln/cX2wljTaP1exjsMJFolfNGa9txAqvbT/zwmX2nFbPYYd2Ew+ntR/wJOHFfvny5VqxYoSVLluj2229Xo0aN1LhxYyUnJ+uXX37R3r17lZ6erkGDBunFF19Us2bNrGw34hwlQoD9WFlSamWfr66U3e5jTSll98/ftuPtxEsosf1tO3Zm9bYTrfhmrXur4ztZJIYAWSkWfjcRfwJO3F0ulwYPHqzBgwfrwIED+uKLL/T999+ruLhY9erVU7t27dS+fXvVqBHUsHkgZJQIAfYRiZJSK/u8laXskWBlSWmgfA0Tqa6cl20nuqzedswaZuF+b+X1avWxAMcavrHtA5EVUpbdtGlT9e/f3+y2AAAcLFoHa5EsZbfjWFO7CKec1ykH+jAPd+MAgOBweRwAELZolpRGspTdrmNNnYxyZHuqfEJMsuf8DgAQL0jcAQCmoKQ0eCRHJ7Ht2E8kr4jH820uASBQJO4AgJjhtFJ2yoUB7sYBAIEgcQe8OhHic4hl3ma+dj/u1PixdmWTUnbAfFwRB4Ln5H1u+WowJ7Y/VpG4A/9TPjlq0uQ/Qb8Hsc3XzNeSlJmZ6dj4WVlZysnJYUdsAyRHsCuuiAPBcfI+19/xiBPaH8sSgn3D8uXLdf311+v222/Xxo0bKzx35MgRjRkzxrTGAYCdWL2jcnp8hOfX5KiGpKT//Xfy718TegCAEzh5n+vktseyoK64L1myRH/72980ePBgHT58WKNHj9b111+v8ePHS5JKSkr02WefWdJQmCOSt01ymvKf88cf+8h39zjhuSLvbd0EVu7sq9w++mX4vkq7zIzt/nek4ptRGeFr5uvU1FQZhqGsrCxHxm/QoEHc9HE4nX1/N4F4EeyEmvF0HFmek/e57rYXFBSooKDAkvZTih+aoBL3xYsXa9asWRo0aJAkaePGjZo8ebKOHTumG264wYr2wWSRvG2Ss7mvegXHX3lR586dPf8OpBQ/GmX4/kq7FixYYEnsSMQ3o9Rc8j7zdVpamvLz8x0bnx0k7CzYIUwMXwKsFeyEmvF7HOnsfa7L5aryvZnVfkrxQxdUZrJ3794KnbVz585atGiRxo4dq+PHj+uaa64xvYGAm1Num+T0H5vA2h/alS9KwQEAAOIbx2uhCSpxb9iwofbt26fTTz/d81irVq20aNEiXXnlldq/f7/pDYR14vW2SVbO3B1oubPvUnz/ZfhW81faVf6qbyhXvqorvfJ24iWUtnuLn5KSEnapOeBETjnh6UtgQ5ii+7tpD9wJBZG3cOFC1axZU1LFcueioiKNGzcumk1DCCI1DCKSpfiVj/XttH8LRVCJe48ePfTGG2+oR48eFR5v3ry5Fi9ezMR0DhOPt02yTzl1aKX4kWBlaZeVpVf+4sM/xizGrti6T7x9fzejgTuhINpq1qyplJSUaDcDJonkMAhK8UMT1B7wuuuu07fffuv1udNOO03PP/+81q1bZ0rDAH/CuW2SUztrtHHlK3YxZhEAAMSKWD0GDSpxb9asmZo1a+bz+aZNm+ryyy8Pu1GxqPIVLcMwVFhYWOE1KSkpFTa0cM48RWtmcCtnmix/pSDUe8paPXO3fVg9+zJXvgCn4T7xscWsO6HYA6X+gJ04eRiEv2N9s3OVSJfih3TkvWLFCq1cuVLfffedJKlFixb6/e9/r4EDB5rauFhS3RUtb0K9qhXNmcHDLT+xTym78zD7MszgK7kjsXO+UE94wgmcdzKVUn/Avpw+DMLXsb4ZolmKH9SvfElJia677jp98MEHOuecc3TOOedIkr799lvdfPPNeuONN/T444+rRg1n7TxikZVne5gZHIhd8Tj3BYDqcEUckVG5QtVJk2kifkRrmwsqw37yySe1fft2LV++XG3atKnw3LZt2zRx4kQ9+eSTmjRpkqmNjDULFy5UcnKyioqKKjxes2ZNFRcXh12C4m9mcCtn7jZjJkhmBg8dY9ABAGax+op4bJX6wyz+KlSdNZkmYlUkSvF9CSpxf/311zVt2rQqSbskZWRkaNq0aXrwwQdJ3KvhLj/xNubczNm1mRk8NgQ643fF1zivbBIAosvquUHgG/ssAM5hZSm+P0H9Su7fv18dOnTw+XyHDh24lztgslBu5wQAqB5zg/jGFXFrhHYyPj65J0irPAGYEyZIA6wQVOJet25d/fDDDzrllFO8Pr9//37VrVvXlIYBAADADrgibhZOxgfO6ROkAWYL6le4d+/eevTRRzV//nwlJSVVeK64uFizZ89W7969TW0gnIVJRazl6/YckvTLL79o4sSJUWsbADgNc4MAcIpAqzXK4zg7tgSVuE+ZMkXDhw9X//79NWLECLVq1UqStGPHDr3wwgsqLS3VAw88YElD4QxMKmItf2efCwsLI9waAIglXFVGZHEyPvKcfIEplGoNjrNjS1B7qCZNmujll1/WQw89pH/+8586duyYJKlWrVq66KKLdOONN6px48aWNBQAAACIFZyMjzwuMMHJgj613KhRI91333269957dfDgQRmGoYYNG9rmbJQT+PsxjqUf6p9+6vu/e0K7J/M5uY24XKVq3PjfFV4bSvkPAADOET+z1gdyVTOy+/X4WfeID/6qNZi8L3aFXBPmcrnUsGFDSdInn3yigoICZWZmql69ema1LaaUn4n26quvDvo9TnQyaa+6iXn7WEzWAgBW85ekkMBYIV5nrQ/2qqYV4nXdB3shxE6l4JFm9qz1kRyDzsR98SmoxP3JJ59Ufn6+brjhBklSWVmZrr76an300UeSpPr16+vpp59Wu3btTG8o/OP2IgCAyoJNXiq/B4CzBHshJJ5Lwc1OfhmDHtvsMD9CUIn7G2+8oUmTJnn+/te//qUvvvhCS5YsUatWrXTrrbfqscce05w5c0xtZCwo/8U99dRTfsc0ua/IB/NlW33FOpInBnyV/9h5ohb7lQUieriqGdsouUXg7DprfST3Wb6ualq9T7frugfgTHaYHyGoxH3fvn0Vrqa/++67uuSSSzwfYtKkSZo8ebKpDYxFKSkpjitviWQpu68zoHYe/2+HskBED1c1Y5uTS24DS14kcxIYTmr4Z59Z6yO5z7LHPt0+6z6SfF0IYQy09RiDDisE9StWeWeem5tb4WxpgwYN9Msvv5jTMoSM24sAAKoyP3lx8kkNwJtYGnrIOOjoYd3HNrPnRwhUUHvwjIwMvfnmm5o0aZK++uor7d+/v8KZ2X379qlRo0amNxLBsfr2Ik48g2u3skCXq7TcJH2/zrrvcpWa0gZElj2ualb3HEJFyS1iWbRK2e0qtApDqk2AeBKtEzNBJe6TJk3SpEmTtGrVKu3bt0+XXnqpmjVr5nn+7bff9vtjh9hgj7OIwSUvdisLrHw7PMSS6F/VrPwemMnKklvnnZThpIbz2aOU3dmsrzbhxACAII8+zj33XL3yyitat26dGjVqpEsuuaTC82eeeaY6depkagMBN5IXALEmtn7X4nMcMWKXv6GHR44cqTBhs9kYhgKgsqD3sGeccYaWLl2qfv36KTExscJz2dnZpjUMscCeZ4ijVRbo3vlL0rp165SamqqCggLPFX/3Y27l/+1k9hsvGKtXNSWubAKxIZT7QcN8gQ49pNrEmXxVlVBtArsKOnFPSkrSK6+8otGjR1vRHsQQs88Qm5W8RKsssHxbUlNTq9wiwttjsSCSdyTwhauasCtOysAbO/xuIhjm/y4zDMUa5fft7tsvB/p6INoSQnnTJZdcolWrVpndFp8OHTqkHj16aPjw4Z7Htm3bpuHDh6tz584aOHCg1q9fH7H2wA5qVPMfADgNv2tOYRiG8vPzK/xX+Yp45cdgjvhc9/wuwPkC7bvu/zhpUlVIPb5OnTr65z//qQ8//FDt27evcvVyypQppjTO7W9/+5syMjJUVFQkSSopKdHEiRN1xRVXaPHixVq5cqUmTZqkNWvWqG7duqYuG6HjDDEqi9YdCcK5qmn1HQmsjG/1MAWnx3c6J2+bVrN624nkFXFfv5vM+u4d1Qjwp/zxwFNPPeWzAtN9NZ7jVPME23dzc3NjshI1HCEl7l9++aXat2+vsrIyffnllxWeM3sD/+STT/Tdd9/pD3/4g1588UVJ0qeffqrCwkKNHz9eCQkJGjJkiJ599lmtXr1aw4YNC2k5oZzVCWaHH8rBgdXxg4kbWvzqzwZbffBn1/je3ucrhtVtNwyjyjCB8ss8ePCgCgoKTPmMZt6RwF97/K/XwK5SlG+/rwPBcA4QIxHfX+xw4gfSdifEt3rbr+65cJJG9/+dtm0Gu27C+d2xetu0QjC/m6EM77LrPjHY+FbGdvq6MfP3KtZfn5KSUu0xidOP9a2Mb7fX+3u/Gf3OqvYbhhFw/hxS4r5o0aJQ3ha04uJizZo1Sw899JC++uorz+N5eXnKyMhQQsKvlf7t2rVTXl5eWMsK1tatWwNOQkLZwVodP5g2EN/c+N7e5+v7jkTb/V3t7tu3b9jxreCObxiGpxrHrfzfGzduDCu+3badYOJbJRbWTSS2fW8q9/lw4jtx/Qezbsz6bTWTt/hmVhI5+buNVPzy/M36HmxFQiysm3D6TKCIb15sp8cPZ90H8rsZbHx/yws3VuV4gQhmmcnJyQG9ztaDY+bNm6dzzz1Xbdu2rZC4Hz9+XLVr167w2jp16ujo0aMhLyvQFVZeu3btAp75O5QzPVbHD2TWd3cbrDpDHK/xvb3P1/dtt7bbMb6/q2ehlpJ6a7+ZdySIRPzyzDzA9dd2p8Y3k7/f7sp9Ppz4Ttw23bGrq3Zo2bKlab+tVm87ZlYSxdLvslXxywt01vdAYzt93YTTZwJFfPNiOz1+OOs+kN/NYOP7W164sSrHC0Sgy9y+fXvAMW2buO/atUvLly/X8uXLqzyXnp6uY8eOVXjs6NGjSk9PD3l5oZT4Wz0LuBXxg51dOyUlxXGf0Qnx7TCrvLflmXmAa9d1H058M0tWIxE/kNihxA+m7U6Jb+ZV00C3zVAPIpy8bbpj5+fn+z3hFmq1QzS2HTMRv/r4VsZ2+rox63jETMSP3fh2a7u3uU2qi2/lHAaBtj+YNtg2cd+wYYMOHDigPn36SDpZyl5cXKwePXronnvu0bZt21RWVuYpl9+yZQv3kQfCZOYBbjQwiRNCZeZVUwAAEFn+Jr/zdqLYiZPf2TZxHzBgQIWVvGrVKi1fvlxz585Vw4YNlZycrIULF2rMmDF66623tHfvXl100UVRbLEzcF9QxDIzrzoC4Qp0ZvN4Z2alD+Ak/vZNleduAQDbJu6pqakVSqLq1KmjpKQknXLKKZKkuXPnasaMGZo9e7aaN2+uOXPmqF69elFqrVNxD1AAsIq/s/8St61yc3qlDxCM8kMW3bccC+Bd1jQGiFG+5mWx+vbDVnNM1jZ06FANHTrU83fbtm21dOnSKLYIgHWqnzgRAAAAqCxWh785JnEHIsnlKtWvJ8Xd/3B5nvPH1xUirhwFLpCJE8tftQB+Zc+TPsy/AECqOATxqaee8plcHDlyRJMmTXK/KwItA2B3JO6AF40b/zuo1wdb+kbSCZgn2LtlRKP/Mf8CgMpSUlIYJgIgYCTugIPEy0Q2TJwIAIC9UWEIRBaJOxwpnFJ2X9xlrJK0bt06paamqqCgwDOBlPux8tx/B1L6VlhY6LkaH0zSGZ8T2TBxYuwyv5Sdu2UAQGREssLQyScG/LXRCe2HPXFkDEcKtpQ9EOUP5lNTU6vc29HbY974K30D4lFkS9k56QMATuXkoYehXGixU/thfxzdIO447QwuE9kAAAA7sLLCEIB/JO5wjHBK2Z18Brc8JrKBE1HKDiCafA2vC3VoHU6yosLQyScGAr3QYtf2R5LTLqLZBYk7HMOsUnYA0UQpO4CKrJi3pjwrhtfBek4eeujktlslVi6iRRNHT4gLTj6DCwBALCOxBoDqkbgj7nAWFACA2Bbs8LrKQ+0AmIuLaOEjcY9B8XKvb8MwVFBQ4Pm7oKBAqampdHQxng8AYG+hzltTfr/vD8PrAPviIlpoSNxjRLzd69swDGVnZys3N9fzWM+ePZWVlaWcnJy4T94pOwQA2BmJNQAEJyHaDQBCFe/JOQAAAID4wBX3GBFv9/p2uVzKyclRQUGBp9rA5XLFdam8GeP54mWYRah8rR/WDQDAbrjlVuzy9x2a8f06fdtxevt9IXH3wurOYLV4ude3y+WijK6cUMsO422YRbCCXz/xs24AIJY58WQ2t9yKXaEcrwXz/Tp923F6+wNB4u6FFZ0BAAAA9sbJbAB2ReIOxLlIDrOwunTJilL2QNaPE4agOPHKEWAGtn1nCOVOMU6vkLQCt9yKXYEer4X6/Tp923F6+wNB4u6FFZ0BcAIrhlmEdvXCyvihXxnxtX7seuKBK0eBi9XxcPGKbd9ZgrlTjNXlwrE0ZxC33IpdVn+3Tt92nN5+X0jcvYjVLxuA/TCGPnqsPqkEIHB2vBgSL3MGAXAGEncELVrlzuwknSnY0iUr4jvhyogV7DAMQjKnHJlyZwQjlq6aOl0g+/Rg7hRjdbkwANgViTsCYrdyZyYGdKZolXbZ+aRPpE482GcYhHUz3AYb3+qTSrAHrppGXij79FDuFEOFJIB4QuIOADbhxBMPsYIEAAAA2BmJOwJih3Jnyt4Ae7G6HJly5/jAMAj/rLhbhtXx2acDgPlI3BE0ZrIEYtmJIB8/yepyZMqdY0sszfpuxbwswa4fl+uEfn2L+x+u/z1XGnZ8K+7GAQAIDok7AMS58gfxTZr8J6jXA/HIbnckaNy4+n4bDperVIbhvipe/YkBAID5SNwRd9zlf+Vnri3/uJ2Vb6MT2w/At5PJkfsveyVHZpdTMwzCv+AnrbQ2vtUnBuKVv316cXFxVNoEwL5I3GGZXw9C7XUAOm7cuKguPxxObjvsq/xB/I8/9pH3XcMJz9V4xqNao3Hjf1saP9gTA5Eqp3biMIhI3pHA1/op//2sW7dOqampKigoUM+ePSs8Vl7lv/3Ft+u6jyXs0wEEg8QdlrH6IBSAFWqIXUNs4jfZGtEaw13+5EFqamqVW6l5eywYNWvW9Pw7nBMDAFCZr2oTqkf94+gMcSE1NVW5ubmev5108FG57ZKz2g/A99VLs66aWiH4cm1nV2LYtUosWqw+MRCvAt2nl38MiDVOrzaxYlLSQJC4wxJ2PAD1dYBh94MPf22X7N9+IF4FW2qekpJi26um8VBOTUUCIoF9OuBMwe7TrZjIl8QdluDsPABEH1dNAeejGgSxINgKUqpHqyJxR9AYl+Kfk2etDxQHEUD1gp3ALJxJ/6JVtud0lSsSJNmiSgwoj2oQazj5eM2JdxlyerVJJPfpvpC4I2hOH5ditXhYPxxEAMGxYgIzO5TtOV3lioTy7H4QCSA8Tj5ec3LbY0G0JiUlcQcAALAYlUoIhpMn1QWizcnVFP6QuCMgjEvxLx52sJSU+t4RFBcXR61NiF92KNtD4KhUQjCcPKmunTn5eI27DAUuVisSSNwREKePS7FaPOxgY6Wk9NerXlKwV75idUcA54tW2R4AOImTj9c4FgeJO4C4wlUvAJFmt1ukIjD+JgCj0sp6Ti53duLkceU5cSJqJ1dTBIrEPQqc3pmBeBLIjqD8YwDsxQ77XG7F50xUWUWXk9e/k9suObP9Tq6mCBSJexQ4sTMATlZ5fH4wV77iYUcAxDL2uQCAWEDiDiBiolV6VXl8Ple+AADeBDoBGJVW1nByubPTJ49jImr7I3GPEKd3ZsAM8XDlK5zJ7+zAV/ud0HY4kxXjiEPd5xYUFIS0PMQOJgCLLidXuTl923F6++MBiXuE0BngBHYYC+p0Tp/8zunth/NYcUKPfS4AINaQuAPwsOIAOpJlh06cBRUAACuwTwRiC4l7DAq07PDXkljnlfPCOSJ55StapfhOHwoT7OR9dmo7nCmUE3oM4wCCEw/D04B4QuIegwL9oaYkFhJjQc3g9LJcJu9DpIXSZ9hnAfbCFX0gskjcgTjn9KSTWVBRHpVEAOJZJPeJXNEHIovEPUYE+kNtGIYKCwslSSkpKSosLHRMOS/gjdNPPATL6bPWW42rsrGFE3NAcOJtnwjEExL3GBHMD3V6enqF93l7DQB7IjFFPCEJAeyFk2nAr8NEIn0HJhJ3ALZDuTOCUXlyPUlUEiFiGOeLeMLJNCB6w0RI3AHYDleVKwp21ncpviYQrFw5VB4HkbAa43wBAJFA4h4lhmFUOKguKChQampqhQPQeMXVi/C5ty+2sdjArO+INl9lgd5vMSpxuzbzsE8EgOirPEwkGrf9tW3iXlxcrLvuuksfffSRDh06pNNOO00TJkzQ4MGDJUnbtm3TjBkz9M0336h58+a688471bVrV1OW7e8+6GbsKA3DUHZ2doUvv2fPnsrKylJOTk7cJ1ZcvQiPt+1Lsv82ZocfxGiL1pgpoDqB/C7HU6UMM3cDQHzxN0wkUhdQbJu4nzhxQk2aNNGzzz6rZs2aacOGDZowYYKaN2+uDh06aOLEibriiiu0ePFirVy5UpMmTdKaNWtUt27dsJcdiZ2kHRMnxA4nbl92+EGMNg7QAWdgnC8AINJsm7inpaVpypQpnr+7du2qrKws5ebmKj8/X4WFhRo/frwSEhI0ZMgQPfvss1q9erWGDRsWxVYHxuVyKScnRwUFBRWurJlZxuy0UnxmKTVP+e1LkmXbGJzF6kqiQFFO7RyBVMFUvsWoy+Xit9sEkdwnWl3pY2V8u/yuAUAk2DZxryw/P19ffvmlxowZo7y8PGVkZCghIcHzfLt27ZSXlxfWMtyzEbsVFBSob9++kqR///vfVXaMhmEoPz8/rGWWT6LMmkjKMAyNHTtWGzdu9DzWs2dPZWZmauHChT6XGezyA3lvOPEDXXa47/cXK9T22yW+FNo2ZvV36+T4Zn+3hmFU+O0J9Hcn2M8Y6NV893uCjR+oQMqpvbUhUJXfE+h3ZKZQ2x/Muo9E/EC4TwgG24bqnovW76Zdf3cCXXYo6zCQ34ZIxQ+E1b9r1S0z3P4daPxQXhNO/HBYGd/JbSd+dOM7pe2GYQR8Uc0RibthGJo2bZo6deqkc889V5s2bVLt2rUrvKZOnTo6evRoyMsoKSnRrl27KjzmvoogSbt371ZKSkrI8SOp8tV2t/z8fG3ZsqXCxlH+M27dujWozxjIe62OH6pAY4faBrvED5Udvlu7xo/kd+vvdyfYzxgodyyr4ofahmDe6y2WN3ZrfzDrXgq+/cHG97e8WP3dtOvvjtnx7bptBsLq37Xqlhlu/w40fiiv8cUOxwx2jE382I7vpLYnJycH9DrbJ+6GYeiOO+7QgQMHPFeL09PTdezYsQqvO3r0qNLT00NeTlJSklq3bl1huYcOHfL83bJlS9WvX98xZcYvvPCCCgsLK5SOucsYyyuf4Ldr1y7kKye+3mt1/FAFGjvUNlT3Pnd5afnXBbONWbluAo3v9G3Hqu/WTvErX813v8/fFf3y5c7VxQ/0DHP529m5l+mvHd7aEKh27dpV+TuQdWgm9zLLxw9kZnBv7zOz/cHG97c8u2/7ob7Prr87ZscPttInmN+FcOIHwurfNW+8vS/U/hdo/FBe44sdjhnsGJv4sR3fKW3fvn17wK+1deJuGIbuuusuff3113rmmWc8E720adNGCxYsUFlZmadcfsuWLcrOzg55WeUnmvE2K3ffvn1tPSO3N8GeyAhnMp1A3mt1/FAFGjvUNlR+n69Z30PdxqyeBMkO361d40d62wnldZV/B8oP72nQoIGl7Xcrvz27lxlKOwJhh/u4e1tmICW9Vt/qz8z4Ttj2w32fXX93zIpf/rfBit+FcOP7E83fNTts03Y9nrI6vpPbTvzoxrdz24M55k+o/iXRM3PmTG3cuFFPPfWUatWq5Xm8e/fuSk5O1sKFC1VcXKwVK1Zo7969uuiii0xbtlOSczgT2xcAAACAQNn2ivu+ffuUk5Oj5ORkXXDBBZ7HJ0yYoGuvvVZz587VjBkzNHv2bDVv3lxz5sxRvXr1TFl2JGZ9R/xi1ncgPnC3DAAInbc7JEky7VgpGvHNPM6zMr47tlPjxyrbJu7NmjXTN9984/P5tm3baunSpZYtv7p7tALhYPsCYh/3+gaA0HgbVug+6WnG0NVoxTdr2K2V8X0N6XRK/Fhm61J5AAAAAPHH6uSN+NGJHYn4scq2V9xhX5S3AKjM16zplZ+zq0BmfbczdzuduO6BcFhdjmw1p7ffKr6GrUrmlLJHK75Z362V8a0e0hmJIaNOH2bhC4k7gkJ5CwBvApk13c5oP+A8VpcjW83p7bea1cMKiR+d2FbHd/owC38olUfQ4n1HAgAA7MHpxyRObz9gR7Fa6s8VdwQlnmZED7V8LVrlM0CkBTtruvs9obCiFD/Q9pd/zE4qt9+qdY/Y5uR9ViTuAuTrWMAMVrefoY2IR04fZuEPiTuCFg8zoodavhbN8hkg0iI5a7oVpeBOn/XdX/vt3nbYQyzss6JRcpuVlaUFCxaYsgyr2s/QRsQzJ5f6+0OpPOBDqDs0doQAAKdgn+Wfk9ePk9sOoCquuANehFq+FonyGWagRbwItRS/fP8wm8tVqpNd23A/4nkcsIKV5c7RLPl0An/HAlb+zpghnoY2AvGCxB3wIdQymGiV7VH2hlhjx1L2xo3/HdHlIb5Fotw5Hoa/hcPJ68fJbQdQFYk7LOPkCW/sjHUHAPGD33wAgETiHtesTKxjYcIbO4rEDLoAKmL2dkQL5c6wM2atByKLxD1ORSKx5kfbGpS+AZHF7O2IJn7zYUfMWg9EHol7HLPyB5UJbwAAAGIXx3JAZJG4x6lIJNZ2vUrA2HsAAIDQMYwDiDwS9zhm18TaSoy9BwAACF88HkcC0ZQQ7QYAkUZiDgAAAMBJuOKOuMLYewAAYCaXq1T/O6SQ5P6HSy5XaZRaBCAWkbgj7lDaBQAAzNK48b+j3QQAcYBSeQAAAAAAbIwr7gAAWMx9N4vKd7RgiA5iXazeySU1NbXKPcwLCgo8E96uW7dOqampFV4PAOEgcQcAwELe7mYhnbyjBXezQCyL5Tu5VDfsLjU1lWF5AExFqTwAABZzanIChIttHwDMwRV3AAAsVP5uFpIq3NHC6eXCgD/cyQUAzEPiDgCAxbibBeIV2z4AmINSeQAAAAAAbIzEHQAAAAAAG6NUHkDQYvX2PoBTWd0n6fMAAEQXiTuAoMTy7X0AJ7K6T9LnAQCIPkrlAQSNg3TAXqzuk/R5AACiiyvuAILC7X2cy1u5M9+Z81ndJ+nzAABEH4k7gKBxex/n8VXuTKlzbLC6T9LnAQCILkrlASBOkJwDAAA4E1fcASBATp5Z21e5sxPaDoSLYSLhYx0CQHRxxR0AAuAuNXfPpi2dLDXv0qWLRowY4UmG7cxd7pyenq709HSlpaVx0I2Y56vvOqXf2gHrEACij8QdAAJEkgs4E303fKxDAIguSuUBIADBzKwdryWlVn/ueF2vCA/DRMLHOgSA6OOKOwAEyFupeeVy83gtKbX6c8freoU5GCYSPtYhAEQXiTsAmCxeD2at/tzxul4BAAAolQcQd6wsuY7XklKrP7fV8d3bBKX4AADAjkjcAcQVd8l1bm6u57GePXsqKytLOTk5piWZaWlpYcdxGqs/t1XxvW0TkvnbBQAAQKgolQcQd0jCUBnbBAAAsDOuuAOIK/Fayg7fym8TktguAATF6qE23oZ3SVXvaAIgtpG4A4g78VrKDt/YJgCEwuqhNr6Gd0liKA8QZyiVBwAAAELEHTUARAJX3GE7zO6MeGWHckj6X2yz8o4KbDvRZeV363RWbptWD7XxNbxLolQeiDck7rAVZndGvLJDOST9L7ZZeUcFtp3oisTdMpwqEtumU++oAcBZKJWH7cTzAQbimx22fTu0Adax8vtl24ku1r9vrBsAsYAr7rAVZndGOJxcqhtMOaRVJbGx0P/sMNwgHFaWO1t5R4Vwt51APjczd/vG3TJ8i4XfNQCQSNxhQ5SEIRSxUKobyLZvdUmsk/ufHYYbhCMS5c5Wfr+hxg7kczNzd/Wc3HetxroBEAsolQcQM+x+YG2WePmcoXD6unF6+0MVyOdm5m4AQDzjijuACuww63QoJavxUg7p9JLYSM3ubNXsy1aWUzv9uw1VIJ/bzjN3W/m7BgCAm8tw76Hi2ObNmyVJHTt2jHJLgOjyVY5q5azTleMH+jo4j9O/W6e3H+bjdw0AEI5g8lBHl8r/8ssvmjJlirp06aLzzjtPzz//fLSbBDieHWad5gA2djn9u3V6+2E+ftcAAJHg6Cvuf/3rX1VYWKj7779f3333ncaOHat//OMf+t3vfhdUHK64A79yl3NaUapbvlTUX3xvbZAoKY0FgW4DdsW2icr4XQMAhCqYPNSxY9zz8/O1atUqLVu2TLVq1VL79u11+eWX65VXXgk6cS8pKZFhGNq0aZNFrQUAAAAA4FclJSUBn7x1bOK+a9cuSVLr1q09j7Vr107PPPNM0LHKT34DAAAAAIDVXC5X7Cfu+fn5Sk9Pr/BYnTp1dPz48aBjdenSxaxmAQAAAABgKsdOTpeWllYlST969GiVZB4AAAAAACdzbOJ+xhlnSJJ27NjheWzr1q1q06ZNlFoEAAAAAID5HJu4p6WlqX///vrHP/6hY8eOaevWrXr11Vc1dOjQaDcNAAAAAADTOPp2cL/88otmzJihDz74QOnp6Zo4caJGjhwZ7WYBAAAAAGAaRyfuAAAAAADEOseWygMAAAAAEA9I3AEAAAAAsDESdwAAAAAAbIzEHQAAAAAAGyNxBwAAAADAxkjcAQAAAACwMRJ3AAAAAABsjMQdAAAAAAAbI3GvxuLFizV06FB16NBB//d//1fhuW3btmn48OHq3LmzBg4cqPXr15sW+/bbb1f//v3Vrl07LVmyxNS279y5U5MmTVLPnj3VtWtXjRw5Ups2bTIt/vHjxzVy5Ej16NFDWVlZGjJkiN5++23T4pf3ySefqG3btvr73/9uavw+ffqoU6dO6tKli7p06aJLL73U1PhlZWV6/PHH1bt3b0/87777LuzY69ev97TZ/V/btm319NNPm9b2jz76SEOHDlVWVpYuvPBCPfXUU0HFri7++++/r8GDB6tLly667LLL9MUXXwQdv7i4WLfddpv69OnjWb+vv/665/lw+m51scPtu/7im9F3/cU3o+9Wt37cQu271cUPt+9WFz+cvusvthl9t7q2h9t3q4tvRt+9/fbbdd555ykrK0t9+vTRP//5T89z4e5zq4tvxn7XV3yz9ru+4pvRd/2tG7dw9rn+4puxz/UXP9x9rr/4Zu13/bXfjP2uv/hm9F1JOnTokHr06KHhw4d7HjOj3/qLb0a/9RXfrH7rK75Zx8u+4pcXTt/1FduMfusvvhn91ld8s/qtv/ab0W+rMODXW2+9ZaxZs8a46667jBtuuMHzeHFxsdGnTx9j3rx5RlFRkbFs2TKjW7duxuHDh8OObRiGsXjxYmPdunXGsGHDjJycHFPbvnHjRuOFF14wfv75Z+PEiRPGc889Z/To0cM4fvy4KfGLi4uNvLw848SJE4ZhGMbnn39uZGZmGj/88IMp8d2KioqMQYMGGVdccYXx4IMPBhW7uvgXXnih8d577wUdM9D4s2fPNkaOHGl89913RllZmfHtt9+atu2Ut337dqNdu3bGvn37TGl7UVGR0aVLF2Px4sVGaWmpkZeXZ3Tv3j3odeUr/u7du40uXboY69atM06cOGG89NJLRvfu3Y0jR44EFf/48ePGo48+anz33XdGaWmp8dlnnxlZWVnGhg0bwu67/mIbRvh91198M/pudesm3L5b3foxjPD6bnXxw+271cUPp+8Gsm7cQum7/uKb0Xf9xTer7+bl5RkFBQWGYRjG/v37jQEDBhhvvvmmKftcf/ENw5z9rq/4Zu13/a2fcPuuv3VjGOHvc/3FN2Of6y9+uPvc6uKXF+p+11d8s/a7vuKb1XcNwzBuueUWY9SoUcawYcMMwzDnWNlffMMwp9/6im9Wv/UV36zjZV/x3cLtu75im9Fv/cU3o9/6i19eqP3WV3yz+m1lXHGvxsUXX6x+/fqpfv36FR7/9NNPVVhYqPHjxys5OVlDhgzR6aefrtWrV4cdW5JGjhypc845RzVr1jS97Z06ddIVV1yhBg0aKDExUaNHj1ZBQYG+/fZbU+InJSWpdevWSkxMlGEYSkhI0IkTJ7Rv3z5T4rvNmzdPF154oc4444yg4gYaP1y+4v/yyy9auHCh7r77bjVv3lwul0utWrVS3bp1w45d2csvv6xzzjlHp512miltP3TokI4fP66hQ4cqISFBrVu3VpcuXZSXl2dK/A8++ECZmZk655xzlJiYqGHDhik9PV1r1qwJKn5aWpqmTJmi5s2bKyEhQV27dlVWVpZyc3PD7rv+Ykvh911/8c3ou/7im9F3q1s/Unh9N5D44fAXP9y+G0zbQ+m7/uKb0Xf9xTer77Zu3VopKSmevxMSErR7925T9rn+4kvm7Hd9xTdrv+srvhl919+6kcLf51YXP1y+4puxzw2m/aHud33FN2u/6yu+WX33k08+0XfffafLLrvM85hZ/dZXfMmcfusrvln91ld8s46XfcV3C7fv+ottBm/xzeq3vuJXFmq/9RXfrH5bGYl7iPLy8pSRkaGEhF9XYbt27cL+QqJh06ZNKisrU8uWLU2NO2LECHXs2FFXXHGFunbtqs6dO5sWe+fOnfrXv/6liRMnmhazsltvvVW/+93vNHr0aH3++eemxd22bZsSExO1evVq9erVS/369dOcOXNkGIZpy5CkEydOaMWKFRo6dKhpMZs2baoBAwZo6dKlKi0t1datW/Xll1+qZ8+epsQvKyvzuh6++eabsOLm5+fryy+/VJs2bUzvu+VjW8FffDP6rrf4ZvbdyvHN7rve2m9m3y0f3+y+6+u7Navvlo9vRd8tH9/MvvvQQw8pMzNTF1xwgfLz8zV48GBT+623+GYKJH44fddf/HD7rq/YZvVbf203o996i29mv63uuw2373qLb2bf9RbfjL5bXFysWbNm6Y477pDL5fI8bla/9RXfLIHGD7XfVhc/3H7rL364fbe6tofbb33FN6vfBvLdhtNvfcW36niZxD1Ex48fV+3atSs8VqdOHR0/fjxKLQrNoUOHdNNNN2nKlClVPk+4cnJytGHDBs2ZM0fnn3++EhMTTYt9xx136Oabb65w9thMDzzwgP7zn//o3Xff1YABA/TnP/85pDOg3nz//fc6evSoduzYoTVr1mj+/Pl65ZVX9Oqrr5oS3+29995TUVGRLrroIlPjDho0SPPmzVPHjh112WWXadSoUfrNb35jSuyePXtqw4YN+uCDD1RSUqIXXnhB+/fvV2FhYcgxDcPQtGnT1KlTJ5177rmm9t3Ksc3mL74ZfddXfLP6rrf4ZvZdb/HN7LuV45vZd/19t2b0XW/xzey7leOb2XenTp2q3NxcLV26VIMGDfL0T7P6rbf4Zqoufrh911/8cPuur9hm9Vtf8c3qt97im9lvq/tuw+27vuKb1Xe9xTej786bN0/nnnuu2rZtW+Fxs/qtr/hmCSR+OP22uvjh9lt/8cPtu/5im9FvfcU3q98G8t2G02/9xbfieJnEPUTp6ek6duxYhceOHj2q9PT0KLUoeEePHtWf//xnnX/++Ro/frwly0hOTla/fv307rvv6j//+Y8pMZctW6b09HT16dPHlHjedO3aVSkpKUpJSdGIESPUvn17vf/++6bETk1NlSRNnjxZaWlpatWqlYYNG6b33nvPlPhur776qgYOHBh2+Vh5O3bs0A033KC7775bmzdv1ttvv63Vq1crJyfHlPhnnXWWHnzwQT3wwAPq1auXNmzYoJ49e6pp06YhxTMMQ3fccYcOHDigRx55RC6Xy7S+6y22mfzFN6PvVtf+cPuut/hm9l1f7Ter73qLb1bfrW7dh9t3vcU3s+96i29233W5XOrUqZOSk5P1+OOPm77PrRzfbL7im7Xf9df+cPtu5dhm73O9td3MfW7l+Gbvc/2tezP2u5Xjm73frRw/3L67a9cuLV++XH/5y1+qPGdGv/UX3wyBxA+n3wba/lD7rb/44fbd6toebr/1F9+Mfhvoug+13/qLb9Xxco2w3h3H2rRpowULFqisrMxTArRlyxZlZ2dHuWWBOXbsmK6++mq1b99et912m+XLKy0tDXkmyMo+/vhjffzxx+rRo4ekk+WaCQkJ2rhxoxYtWmTKMipzuVymlbK7z8pZUe7l9vPPP+u9994Le4bVyvLy8tSyZUtdeOGFkqTTTz9dv//97/Xee+9pxIgRpizj4osv1sUXXyzpZPlS3759dfXVVwcdxzAM3XXXXfr666/1zDPPKC0tTZI5fddXbLP4i29G3w2m/aH0XV/xzeq7wbQ/lL7rK74Zfbe6tofbd33FN6vv+mu/WX23vNLSUu3evVvnnXeeJftcd3yrlI9vxX7XX/vD3e+6Yx8+fNiSfa6/tpuxz3XHHzlypCemmSq33+z9rju+Vfvd8u0Pp+9u2LBBBw4c8CSHxcXFKi4uVo8ePXTPPfdo27ZtYfVbf/H//e9/q1atWgHHCiW+pLD6bbDtD7bf+ovfrVu3sPpusG0Ptt/6i//ss896YoYqkPaH02/9xZ82bZol/ZYr7tU4ceKEioqKdOLECZWVlamoqEglJSXq3r27kpOTtXDhQhUXF2vFihXau3dvUGUWvmJLJ7/8oqIilZWVqbS0VEVFRSotLTWl7e6Dh7POOkt33XVXUDEDif/VV1/p448/9mzAS5cu1RdffKHu3bubEn/atGlauXKlli9fruXLl6tPnz4aOnSoHn30UVPi79+/X+vXr/e0/6WXXtKXX34ZdCm0r/jNmzdXjx499MQTT6ioqEh79uzR0qVLgzoj6m/bkaTXX39drVq1UseOHYNqc3Xx27dvr7179+r999+XYRj64YcftHLlSrVr186U+JK0efNmlZaW6siRI7rnnnt0+umnq1evXkF/hpkzZ2rjxo166qmnKuxczOi7vmJL5vRdX/HN6ru+4pvVd33FN6vv+opvVt/1Fd+Mvutv25HC77u+4pvVd/21P9y+e/ToUS1btkzHjh1TWVmZPv/8cy1ZskQ9e/Y0pd/6iy+F33f9xTej7/qLH27f9RfbjH7rL74Z/dZffDP6bXXbjhRe3/UX34y+W137w+m7AwYM0Jo1azzbx5QpU5SRkaHly5erd+/eYfdbf/HT09PD7rf+4huGEXa/9Rd/165dYe9z/cW/5557wuq7/mIfOXIk7H7rL37btm3D7rfVbTtSeP3WX/ysrCxT9rlVhDUnfRyYPXu2kZGRUeG/W265xTAMw9i6davxxz/+0ejYsaPx+9//3vj0009Niz1q1Kgqz73yyiumxH/11VeNjIwMo3PnzkZmZqbnv88++8yU+F988YVx+eWXG5mZmUbXrl2NYcOGGW+//XZQsf3Fr+yWW24J6fYWvuLn5eUZgwcPNjIzM41u3boZf/rTn4yPP/7Y1PYfOHDAuOaaa4zMzEyjd+/exrx580yLbRiGMXDgQOPpp58Ous2BxF+1apUxcOBAo0uXLkavXr2M22+/3XOLGTPijxo1ysjMzDTOPvts469//avx888/B93+vXv3GhkZGUaHDh0qbONz5841DCO8vltd7HD7rr/4ZvRdf/HN6LvVrZ/yQum7/uKb0Xera384fTeQdRNO360ufrh9N5BtP5y+e/ToUWPMmDFG165djczMTKN///7GvHnzjLKyMsMwwt/nVhc/3L7rL74Zfddf/HD7bnXrprxQ+q2/+Gb02+raH+4+N5D1E07frS5+uH03kG0/3P2u2yuvvFLhllvh9tvq4ptxvOwrvlnHy77im3W87Ct+ZaEeL3uLbdaxsq/4hhF+v60uvmGEf7zsL74Zx8uVuQzD5KmsAQAAAACAaSiVBwAAAADAxkjcAQAAAACwMRJ3AAAAAABsjMQdAAAAAAAbI3EHAAAAAMDGSNwBAAAAALAxEncAAAAAAGyMxB0AAAAAABsjcQcAxJU+ffpo6dKl0W6Gx6233qq//vWv0W5GWPbu3au2bdtq9+7dkqRXX31V559/fsjxglknjz32mLKzs0NeVqg2bdqk/v37q7S0NOQYwbb9888/18CBA1VWVhbyMgEAzlQj2g0AAMAsbdu29fv8c889p5dffllpaWkRalH1brvttmg3Qfn5+Zo3b55WrVql/fv3q0GDBurcubPGjx+vTp06mb68V199VdOmTZMkJSYm6pRTTtGgQYN03XXXKSkpyRbrpDqPPPKIxo8fr8TExJBjjBs3TqNHjw749Weffbbq1q2rFStWaMiQISEvFwDgPCTuAICY8eGHH3r+PX/+fG3atEmPPfaY57G6desqOTk5Gk3zqXbt2lFdfmFhocaMGaOCggL99a9/Vfv27XX8+HG98847evDBB7Vo0SJLltu4cWO99tprKisr04YNGzRt2jTVrFlTkyZNivo6qc7OnTu1YcOGCttWKNLT04N+z+DBg7VkyRISdwCIM5TKAwBiRuPGjT3/paamKikpqcJjycnJFUrl3SXeq1ev1tChQ9WpUyddddVVOnTokFauXKl+/fqpW7duuvfee2UYhmc5Bw8e1NSpU9W1a1f16NFDU6dO1aFDh3y266uvvlJ2drYyMzPVrVs3jRo1Sr/88oukqmXhffr00cKFC3X99dcrMzNTAwYM0EcffVQh3tq1azVs2DB17NhRPXv21J133hly2xYuXKgdO3bo2Wef1UUXXaRmzZopIyNDEyZM0Jw5cyRJ//3vf3X99derV69e6tKli0aOHKktW7YE/sV4kZCQoMaNG6tp06YaMGCABg0apHfffdfrOsnPz9fMmTPVq1cvderUSZdffrk2btzoNe769evVrVs3vf7661VK+CXpk08+Udu2bXXixAlJv5arL1iwQOecc466deumhx9+uML3Xdlbb72lrKws1apVy/PYrbfeqqlTp+qhhx5S9+7d1bNnT73yyiv65ZdfdP3116tLly4aNGhQhfVWuVR+9OjReuCBB/T//t//U5cuXdSnTx/961//qrDsCy64QLm5uTpw4EAAaxkAECtI3AEAcW/OnDm69dZb9eKLL2r//v2aMmWKXn/9dT3xxBN68MEHlZOTo3feecfz+uuvv16S9Pzzz2vRokU6evSobrrpJp/xb7rpJmVlZWnFihXKycnRoEGD/LZnwYIF6tOnj5YtW6auXbvqpptuUnFxsSRp+/btmjBhgnr27Klly5Zp/vz5Ouuss0Ju28qVKzV48GA1atSoynN16tSRdPKqfNeuXbVw4UK9+uqrOuusszRx4kQVFRX5/RzBqFmzpieZruz222/XunXr9Le//U1vvPGGrr32Wq/jvD/66CNde+21mjlzpgYPHhzwsrdu3aovvvhCzz33nGbNmqXFixfrtdde8/n63NxctW/fvsrj7m3kxRdf1OjRo3XHHXdo6tSp6tevn1577TW1bNmy2mEAL774os4880wtW7ZMl19+uaZNm6aff/7Z83zTpk3VuHFjbdiwIeDPBwBwPkrlAQBxb8KECerevbsk6Y9//KMefvhhrV27Vg0bNlRGRoZ69OihTz/9VH369NFnn32mnTt36plnnlGNGid3o7NmzdL555+vH374QaecckqV+N9//70uvPBCNW/eXJLUpk0bv+25+OKLddlll0mS/vKXv+ill17Srl27lJGRofnz56tXr176v//7P8/rf/vb30pSSG3bvXu3/vCHP/htz+mnn64xY8Z4/r7jjjvUtWtXbdq0Sd26dfP73kB8/fXXeuONN7yWf+/Zs0dvvPGGXn75ZXXs2FGS1KJFiyqve++99zR16lTdf//96tevX1DLLysr0z333KO6deuqTZs22rJlixYvXqyhQ4d6ff3+/ft1zjnnVHn81FNP1dSpUyVJ11xzjZ588kk1b97ccxJh/PjxuuKKK3Ts2LEKV+vLy8rK0lVXXSVJmjhxop566ilt2rRJF154oec1jRs31v79+4P6jAAAZyNxBwDEvfKT2jVs2FANGjRQw4YNPY81atRIBw8elCRt27ZNBw8e9Jqw7tmzx2tyPGrUKI0bN07nnnuuevXqpQEDBqhBgwY+25ORkeH5d+PGjSXJc9U1Ly9Pl156qdf3hdK2QJSUlOixxx7TmjVr9NNPP6m0tFQFBQX6/vvvQ4onST/++KO6dOmi0tJSnThxQhdffLH+8pe/VHldXl6e0tLSPEm7Nzt37tTkyZP1yCOPBJ20SydPBNStW9fzd6dOnfTcc8/5fH1xcbHXuRLKn5BJTExUvXr11Lp1a89j7m3q0KFDPhP38t99jRo1VL9+/QpX3KWT1QmFhYXVfCoAQCwhcQcAxD331WlJcrlcFf52P+a+7dfx48fVokULPfnkk1XiNG3a1Gv8qVOnesZwL1++XLNnz9aLL76oM844w+vrk5KSKixbkmfMtb+x16G0rWXLltq5c6fPmNLJif5ee+01zZgxQ61atVLNmjU1bNgwn6XtgWjUqJGef/55JSYmqkmTJj4nDfT3ed2aNGmiZs2a6dVXX9WFF17o+f4SEhKqxPDWZvc6DlT9+vV19OjRKo972268fZf+bufmLUbldXDkyBHVr18/qDYDAJyNMe4AAAShXbt2+v7771WrVi21bNmywn8pKSk+35eRkaFrrrlGL730kho1aqQ1a9aEtPyMjAx9+umnprXtkksu0YoVK6pc1ZXkSU43btyoSy65RP3791dGRoaSk5N15MiRkNrvlpCQoJYtW+r000/3O9N/RkaG8vPztXnzZp+vSU9P14IFC7Rnzx7dcsstnsTYXdXw3//+1/Pabdu2VXn/7t27PZMFStLmzZvVqlUrn8tr27atvv32W98fzkLFxcXas2eP2rVrF5XlAwCig8QdAIAgnHvuucrIyNB1112n9evXa8+ePVq7dq1uv/12r68vLCzU3XffrfXr12vfvn167733tH//fr+JoT9//vOftXbtWj3yyCPasWOHtm7dqsWLF4fUNunkvcTPOOMMDR8+XG+++aZ27dqlvLw8LViwQJMmTZIkNW/eXB988IG++uorffXVV7rllltUs2bNkNofrObNm2vgwIG66aabtHbtWu3Zs0dvv/22vvjiiwqvq1+/vp5++mlt3rxZd9xxhyQpJSVFHTp00Lx587Rz50795z//UU5OTpVlJCQkaMaMGdq+fbtWr16tRYsWaeTIkT7b1KtXr6hNDrdp0yalpqaqQ4cOUVk+ACA6SNwBAAhCQkKCFixYoFatWum6667TpZdeqlmzZvm893hCQoJ+/vln3Xjjjerfv79mzpypyZMnhzQWW5Jat26tuXPn6oMPPtCQIUM0btw4T6l7sG2TpNTUVC1atEiXXnqpHn30UQ0cOFBXX321Nm3apFtuuUWSNGnSJJ1++ukaMWKE/vKXv2j48OGqV69eSO0PxaxZs9SjRw/deOONGjhwoJ544glPGXx5jRs31tNPP60PPvhA9913nyTpnnvu0c8//6zLLrtMTz/9tCZPnlzlfe3atVOHDh00cuRITZ8+XdnZ2T4nppNO3pLt6NGj+vrrr837kAFatWqVBg4c6LdKAQAQe1xGIIPHAAAAYtBjjz2mdevWacmSJUG9b968edq1a5fnBEEkHDt2TBdffLFefPFFzx0KAADxgSvuAAAAQRozZoxatmzpmbQwEvbv369bb72VpB0A4hCzygMAAAQpNTVV1157bUSXmZGRUeF2cQCA+EGpPAAAAAAANkapPAAAAAAANkbiDgAAAACAjZG4AwAAAABgYyTuAAAAAADYGIk7AAAAAAA2RuIOAAAAAICNkbgDAAAAAGBjJO4AAAAAANjY/wdOl9b2RpMYGQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(12,4)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=.8)\n",
    "sns.boxplot()\n",
    "palette = ['lightgray', 'navy']\n",
    "ax = sns.boxplot(data=df14_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"Outcome\", showfliers=False, palette=palette)\n",
    "ax.set(ylim=(0, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e54d0f31-2616-4553-8367-d2290cd65a64",
   "metadata": {},
   "source": [
    "### Drop the whiskers and outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d82b72b-7652-4a76-ab2d-e45bdebb98b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd6073ac-51b8-4e90-9ecb-a94b9e14f2a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(12,4)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=.8)\n",
    "sns.boxplot()\n",
    "palette = ['lightgray', 'tomato']\n",
    "ax = sns.boxplot(data=df14_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"Outcome\", showfliers=False, whis=0, palette=palette)\n",
    "ax.set(ylim=(0, 100))\n",
    "ax.figure.savefig('figures/fig1_nowhisks.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4aa955-6f74-496e-b7b0-f9c5a6bf809d",
   "metadata": {},
   "source": [
    "### Separate CPC12 vs. Not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc8735f6-1910-4ca6-9ec6-f095f274a9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time since Call Pickup (min)', ylabel='rSO2 (%)'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1w = df[['BOx@0min (c)', 'BOx@1min (c)', 'BOx@2min (c)', 'BOx@3min (c)', 'BOx@4min (c)', 'BOx@5min (c)',\n",
    " 'BOx@6min (c)', 'BOx@7min (c)', 'BOx@8min (c)', 'BOx@9min (c)', 'BOx@10min (c)', 'BOx@11min (c)', 'BOx@12min (c)', 'BOx@13min (c)',\n",
    " 'BOx@14min (c)', 'BOx@15min (c)', 'BOx@16min (c)', 'BOx@17min (c)', 'BOx@18min (c)', 'BOx@19min (c)', 'BOx@20min (c)', 'BOx@21min (c)',\n",
    " 'BOx@22min (c)', 'BOx@23min (c)', 'BOx@24min (c)', 'BOx@25min (c)', 'BOx@26min (c)', 'BOx@27min (c)', 'BOx@28min (c)', 'BOx@29min (c)',\n",
    " 'BOx@30min (c)', 'BOx@31min (c)', 'BOx@32min (c)', 'BOx@33min (c)', 'BOx@34min (c)', 'BOx@35min (c)', 'BOx@36min (c)', 'BOx@37min (c)',\n",
    " 'BOx@38min (c)', 'BOx@39min (c)', 'BOx@40min (c)', 'BOx@41min (c)', 'BOx@42min (c)', 'BOx@43min (c)', 'BOx@44min (c)', 'BOx@45min (c)',\n",
    " 'BOx@46min (c)', 'BOx@47min (c)', 'BOx@48min (c)', 'BOx@49min (c)', 'BOx@50min (c)', 'ROSC-CPC12']].copy()\n",
    "df1w.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df1w.columns[:-1]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, roscnan = [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno, rnan = 0, 0, 0, 0\n",
    "    cyes, cno, cnan = [], [], []\n",
    "    for i in range(len(df1w)):\n",
    "        if np.isnan(df1w[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1w[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1w['ROSC-CPC12'][i] == 0:\n",
    "                roscs.append('ROSC-nonsurvivor')\n",
    "                rno += 1\n",
    "            elif df1w['ROSC-CPC12'][i] == 1:\n",
    "                roscs.append('ROSC-survivor')\n",
    "                ryes += 1\n",
    "            else:\n",
    "                roscs.append('no ROSC')\n",
    "                rnan += 1\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    roscnan.append(rnan)\n",
    "    \n",
    "df1w_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1w_sns['rSO2 (%)'] = vals\n",
    "df1w_sns['Outcome'] = roscs\n",
    "\n",
    "df1w_sns\n",
    "\n",
    "ns_df1w = pd.DataFrame(df1w.columns[:-1], columns=['Time since ROSC (min)'])\n",
    "ns_df1w['n'] = ns\n",
    "ns_df1w['n - rosc-survivor'] = roscyes\n",
    "ns_df1w['n - rosc-nonsurvivor'] = roscno\n",
    "ns_df1w['n - no rosc'] = roscnan\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df1w_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\")\n",
    "\n",
    "# Drop the bars where n < 4\n",
    "roscdelis = []  # rosc-survivor indices/minute since Call Pickup where n < 4\n",
    "noroscdelis = []  # rosc-nonsurvivor indices/minute since Call Pickup where n < 4\n",
    "nandelis = []  # no rosc indices/minute since Call Pickup where n < 4\n",
    "\n",
    "for i in range(len(ns_df1w)):\n",
    "    if ns_df1w['n - rosc-survivor'][i] < 4:\n",
    "        roscdelis.append(i)\n",
    "    if ns_df1w['n - rosc-nonsurvivor'][i] < 4:\n",
    "        noroscdelis.append(i)\n",
    "    if ns_df1w['n - no rosc'][i] < 4:\n",
    "        nandelis.append(i)\n",
    "# indices to delete in df1w_sns\n",
    "inds = []\n",
    "for i in range(len(df1w_sns)):\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in roscdelis and df1w_sns['Outcome'][i] == 'ROSC-survivor':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in noroscdelis and df1w_sns['Outcome'][i] == 'ROSC-nonsurvivor':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in nandelis and df1w_sns['Outcome'][i] == 'no ROSC':\n",
    "        inds.append(i)\n",
    "\n",
    "df1w4_sns = df1w_sns.copy()\n",
    "df1w4_sns.drop(inds, inplace=True)\n",
    "df1w4_sns.reset_index(inplace=True, drop=True)\n",
    "df1w4_sns\n",
    "\n",
    "# Get ns_df1w4 from df1w4_sns\n",
    "ns = []\n",
    "nrosc = []\n",
    "nnorosc = []\n",
    "nnanrosc = []\n",
    "for min in range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    rosc = 0\n",
    "    norosc = 0\n",
    "    nanrosc = 0\n",
    "    for index, row in df1w4_sns.iterrows():\n",
    "        if df1w4_sns['Time since Call Pickup (min)'][index] == min:\n",
    "            n += 1\n",
    "            if df1w4_sns['Outcome'][index] == 'ROSC-survivor':\n",
    "                rosc += 1\n",
    "            if df1w4_sns['Outcome'][index] == 'ROSC-nonsurvivor':\n",
    "                norosc += 1\n",
    "            else:\n",
    "                nanrosc += 1\n",
    "        elif df1w4_sns['Time since Call Pickup (min)'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    nrosc.append(rosc)\n",
    "    nnorosc.append(norosc)\n",
    "    nnanrosc.append(nanrosc)\n",
    "ns_df1w4 = pd.DataFrame(list(range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df1w4['n'] = ns\n",
    "ns_df1w4['n - rosc-survivors'] = nrosc\n",
    "ns_df1w4['n - rosc-nonsurvivors'] = nnorosc\n",
    "ns_df1w4['n - no rosc'] = nnanrosc\n",
    "ns_df1w4\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(16,5)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.boxplot()\n",
    "palette = ['lightgray', 'chartreuse', 'royalblue']\n",
    "sns.boxplot(data=df1w4_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"Outcome\", palette=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae36fc27-a105-4346-b05c-a2f2d1777b36",
   "metadata": {},
   "source": [
    "#### Plot n < 4 bars, too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc8f9c3f-1049-4f1d-8004-5c04e2cfb442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time since Call Pickup (min)', ylabel='rSO2 (%)'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1w = df[['BOx@0min (c)', 'BOx@1min (c)', 'BOx@2min (c)', 'BOx@3min (c)', 'BOx@4min (c)', 'BOx@5min (c)',\n",
    " 'BOx@6min (c)', 'BOx@7min (c)', 'BOx@8min (c)', 'BOx@9min (c)', 'BOx@10min (c)', 'BOx@11min (c)', 'BOx@12min (c)', 'BOx@13min (c)',\n",
    " 'BOx@14min (c)', 'BOx@15min (c)', 'BOx@16min (c)', 'BOx@17min (c)', 'BOx@18min (c)', 'BOx@19min (c)', 'BOx@20min (c)', 'BOx@21min (c)',\n",
    " 'BOx@22min (c)', 'BOx@23min (c)', 'BOx@24min (c)', 'BOx@25min (c)', 'BOx@26min (c)', 'BOx@27min (c)', 'BOx@28min (c)', 'BOx@29min (c)',\n",
    " 'BOx@30min (c)', 'BOx@31min (c)', 'BOx@32min (c)', 'BOx@33min (c)', 'BOx@34min (c)', 'BOx@35min (c)', 'BOx@36min (c)', 'BOx@37min (c)',\n",
    " 'BOx@38min (c)', 'BOx@39min (c)', 'BOx@40min (c)', 'BOx@41min (c)', 'BOx@42min (c)', 'BOx@43min (c)', 'BOx@44min (c)', 'BOx@45min (c)',\n",
    " 'BOx@46min (c)', 'BOx@47min (c)', 'BOx@48min (c)', 'BOx@49min (c)', 'BOx@50min (c)', 'ROSC-CPC12']].copy()\n",
    "df1w.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df1w.columns[:-1]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, roscnan = [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno, rnan = 0, 0, 0, 0\n",
    "    cyes, cno, cnan = [], [], []\n",
    "    for i in range(len(df1w)):\n",
    "        if np.isnan(df1w[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1w[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1w['ROSC-CPC12'][i] == 0:\n",
    "                roscs.append('ROSC-nonsurvivor')\n",
    "                rno += 1\n",
    "            elif df1w['ROSC-CPC12'][i] == 1:\n",
    "                roscs.append('ROSC-survivor')\n",
    "                ryes += 1\n",
    "            else:\n",
    "                roscs.append('no ROSC')\n",
    "                rnan += 1\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    roscnan.append(rnan)\n",
    "    \n",
    "df1w_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1w_sns['rSO2 (%)'] = vals\n",
    "df1w_sns['Outcome'] = roscs\n",
    "\n",
    "df1w_sns\n",
    "\n",
    "ns_df1w = pd.DataFrame(df1w.columns[:-1], columns=['Time since ROSC (min)'])\n",
    "ns_df1w['n'] = ns\n",
    "ns_df1w['n - rosc-survivor'] = roscyes\n",
    "ns_df1w['n - rosc-nonsurvivor'] = roscno\n",
    "ns_df1w['n - no rosc'] = roscnan\n",
    "\n",
    "# #Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(16,5)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.boxplot()\n",
    "palette = ['chartreuse', 'lightgray', 'royalblue']\n",
    "sns.boxplot(data=df1w_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"Outcome\", palette=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa093d1-711f-4a6d-947a-7eb8c04d770e",
   "metadata": {},
   "source": [
    "### Post-ROSC - FIGURE 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd564b67-e79c-4f72-aac3-09e6a1051721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time since Call Pickup (min)', ylabel='rSO2 (%)'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1w = df[['BOx@0min (pc)', 'BOx@1min (pc)', 'BOx@2min (pc)', 'BOx@3min (pc)', 'BOx@4min (pc)', 'BOx@5min (pc)',\n",
    " 'BOx@6min (pc)', 'BOx@7min (pc)', 'BOx@8min (pc)', 'BOx@9min (pc)', 'BOx@10min (pc)', 'BOx@11min (pc)', 'BOx@12min (pc)', 'BOx@13min (pc)',\n",
    " 'BOx@14min (pc)', 'BOx@15min (pc)', 'BOx@16min (pc)', 'BOx@17min (pc)', 'BOx@18min (pc)', 'BOx@19min (pc)', 'BOx@20min (pc)', 'BOx@21min (pc)',\n",
    " 'BOx@22min (pc)', 'BOx@23min (pc)', 'BOx@24min (pc)', 'BOx@25min (pc)', 'BOx@26min (pc)', 'BOx@27min (pc)', 'BOx@28min (pc)', 'BOx@29min (pc)',\n",
    " 'BOx@30min (pc)', 'BOx@31min (pc)', 'BOx@32min (pc)', 'BOx@33min (pc)', 'BOx@34min (pc)', 'BOx@35min (pc)', 'BOx@36min (pc)', 'BOx@37min (pc)',\n",
    " 'BOx@38min (pc)', 'BOx@39min (pc)', 'BOx@40min (pc)', 'BOx@41min (pc)', 'BOx@42min (pc)', 'BOx@43min (pc)', 'BOx@44min (pc)', 'BOx@45min (pc)',\n",
    " 'BOx@46min (pc)', 'BOx@47min (pc)', 'BOx@48min (pc)', 'BOx@49min (pc)', 'BOx@50min (pc)', 'ROSC-CPC12']].copy()\n",
    "df1w.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df1w.columns[:-1]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, roscnan = [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno, rnan = 0, 0, 0, 0\n",
    "    cyes, cno, cnan = [], [], []\n",
    "    for i in range(len(df1w)):\n",
    "        if np.isnan(df1w[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1w[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1w['ROSC-CPC12'][i] == 0:\n",
    "                roscs.append('ROSC-nonsurvivor')\n",
    "                rno += 1\n",
    "            elif df1w['ROSC-CPC12'][i] == 1:\n",
    "                roscs.append('ROSC-survivor')\n",
    "                ryes += 1\n",
    "            else:\n",
    "                roscs.append('no ROSC')\n",
    "                rnan += 1\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    roscnan.append(rnan)\n",
    "    \n",
    "df1w_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1w_sns['rSO2 (%)'] = vals\n",
    "df1w_sns['Outcome'] = roscs\n",
    "\n",
    "df1w_sns\n",
    "\n",
    "ns_df1w = pd.DataFrame(df1w.columns[:-1], columns=['Time since ROSC (min)'])\n",
    "ns_df1w['n'] = ns\n",
    "ns_df1w['n - rosc-survivor'] = roscyes\n",
    "ns_df1w['n - rosc-nonsurvivor'] = roscno\n",
    "ns_df1w['n - no rosc'] = roscnan\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df1w_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\")\n",
    "\n",
    "# Drop the bars where n < 4\n",
    "roscdelis = []  # rosc-survivor indices/minute since Call Pickup where n < 4\n",
    "noroscdelis = []  # rosc-nonsurvivor indices/minute since Call Pickup where n < 4\n",
    "nandelis = []  # no rosc indices/minute since Call Pickup where n < 4\n",
    "\n",
    "for i in range(len(ns_df1w)):\n",
    "    if ns_df1w['n - rosc-survivor'][i] < 4:\n",
    "        roscdelis.append(i)\n",
    "    if ns_df1w['n - rosc-nonsurvivor'][i] < 4:\n",
    "        noroscdelis.append(i)\n",
    "    if ns_df1w['n - no rosc'][i] < 4:\n",
    "        nandelis.append(i)\n",
    "# indices to delete in df1w_sns\n",
    "inds = []\n",
    "for i in range(len(df1w_sns)):\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in roscdelis and df1w_sns['Outcome'][i] == 'ROSC-survivor':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in noroscdelis and df1w_sns['Outcome'][i] == 'ROSC-nonsurvivor':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in nandelis and df1w_sns['Outcome'][i] == 'no ROSC':\n",
    "        inds.append(i)\n",
    "\n",
    "df1w4_sns = df1w_sns.copy()\n",
    "df1w4_sns.drop(inds, inplace=True)\n",
    "df1w4_sns.reset_index(inplace=True, drop=True)\n",
    "df1w4_sns\n",
    "\n",
    "# Get ns_df1w4 from df1w4_sns\n",
    "ns = []\n",
    "nrosc = []\n",
    "nnorosc = []\n",
    "nnanrosc = []\n",
    "for min in range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    rosc = 0\n",
    "    norosc = 0\n",
    "    nanrosc = 0\n",
    "    for index, row in df1w4_sns.iterrows():\n",
    "        if df1w4_sns['Time since Call Pickup (min)'][index] == min:\n",
    "            n += 1\n",
    "            if df1w4_sns['Outcome'][index] == 'ROSC-survivor':\n",
    "                rosc += 1\n",
    "            if df1w4_sns['Outcome'][index] == 'ROSC-nonsurvivor':\n",
    "                norosc += 1\n",
    "            else:\n",
    "                nanrosc += 1\n",
    "        elif df1w4_sns['Time since Call Pickup (min)'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    nrosc.append(rosc)\n",
    "    nnorosc.append(norosc)\n",
    "    nnanrosc.append(nanrosc)\n",
    "ns_df1w4 = pd.DataFrame(list(range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df1w4['n'] = ns\n",
    "ns_df1w4['n - rosc-survivors'] = nrosc\n",
    "ns_df1w4['n - rosc-nonsurvivors'] = nnorosc\n",
    "ns_df1w4['n - no rosc'] = nnanrosc\n",
    "ns_df1w4\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.boxplot()\n",
    "palette = ['lightgray', 'chartreuse', 'royalblue']\n",
    "sns.boxplot(data=df1w4_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"Outcome\", palette=palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf07e733-2829-46c0-a795-982b5bbd8539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1w = df[['BOx@0min (pc)', 'BOx@1min (pc)', 'BOx@2min (pc)', 'BOx@3min (pc)', 'BOx@4min (pc)', 'BOx@5min (pc)',\n",
    " 'BOx@6min (pc)', 'BOx@7min (pc)', 'BOx@8min (pc)', 'BOx@9min (pc)', 'BOx@10min (pc)', 'BOx@11min (pc)', 'BOx@12min (pc)', 'BOx@13min (pc)',\n",
    " 'BOx@14min (pc)', 'BOx@15min (pc)', 'BOx@16min (pc)', 'BOx@17min (pc)', 'BOx@18min (pc)', 'BOx@19min (pc)', 'BOx@20min (pc)', 'BOx@21min (pc)',\n",
    " 'BOx@22min (pc)', 'BOx@23min (pc)', 'BOx@24min (pc)', 'BOx@25min (pc)', 'BOx@26min (pc)', 'BOx@27min (pc)', 'BOx@28min (pc)', 'BOx@29min (pc)',\n",
    " 'BOx@30min (pc)', 'BOx@31min (pc)', 'BOx@32min (pc)', 'BOx@33min (pc)', 'BOx@34min (pc)', 'BOx@35min (pc)', 'BOx@36min (pc)', 'BOx@37min (pc)',\n",
    " 'BOx@38min (pc)', 'BOx@39min (pc)', 'BOx@40min (pc)', 'BOx@41min (pc)', 'BOx@42min (pc)', 'BOx@43min (pc)', 'BOx@44min (pc)', 'BOx@45min (pc)',\n",
    " 'BOx@46min (pc)', 'BOx@47min (pc)', 'BOx@48min (pc)', 'BOx@49min (pc)', 'BOx@50min (pc)', 'ROSC', 'ROSC-CPC12']].copy()\n",
    "df1w.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df1w.columns[:-2]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, cassyes, cassno = [], [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno = 0, 0, 0\n",
    "    cyes, cno = [], []\n",
    "    for i in range(len(df1w)):\n",
    "        if np.isnan(df1w[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1w[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1w['ROSC'][i] == 0:\n",
    "                roscs.append('Current No ROSC')\n",
    "                rno += 1\n",
    "                cno.append(df['CASSID'][i])\n",
    "            else:\n",
    "                roscs.append('Current ROSC')\n",
    "                ryes += 1\n",
    "                cyes.append(df['CASSID'][i])\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    cassyes.append(cyes)\n",
    "    cassno.append(cno)\n",
    "    \n",
    "df1w_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1w_sns['rSO2 (%)'] = vals\n",
    "df1w_sns['Outcome'] = roscs\n",
    "\n",
    "df1w_sns\n",
    "\n",
    "ns_df1w = pd.DataFrame(df1w.columns[:-2], columns=['Time since ROSC (min)'])\n",
    "ns_df1w['n'] = ns\n",
    "ns_df1w['n - rosc'] = roscyes\n",
    "ns_df1w['n - no rosc'] = roscno\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df1w_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\")\n",
    "\n",
    "# Drop the bars where n < 4\n",
    "roscdelis = []  # rosc indices/minute since Call Pickup where n < 4\n",
    "noroscdelis = []  # No-rosc indices/minute since Call Pickup where n < 4\n",
    "for i in range(len(ns_df1w)):\n",
    "    if ns_df1w['n - rosc'][i] < 4:\n",
    "        roscdelis.append(i)\n",
    "    if ns_df1w['n - no rosc'][i] < 4:\n",
    "        noroscdelis.append(i)\n",
    "# indices to delete in df1w_sns\n",
    "inds = []\n",
    "for i in range(len(df1w_sns)):\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in roscdelis and df1w_sns['Outcome'][i] == 'Current ROSC':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in noroscdelis and df1w_sns['Outcome'][i] == 'Current No ROSC':\n",
    "        inds.append(i)\n",
    "\n",
    "df1w4_sns = df1w_sns.copy()\n",
    "df1w4_sns.drop(inds, inplace=True)\n",
    "df1w4_sns.reset_index(inplace=True, drop=True)\n",
    "df1w4_sns\n",
    "\n",
    "# Get ns_df1w4 from df1w4_sns\n",
    "ns = []\n",
    "nrosc = []\n",
    "nnorosc = []\n",
    "for min in range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    rosc = 0\n",
    "    norosc = 0\n",
    "    for index, row in df1w4_sns.iterrows():\n",
    "        if df1w4_sns['Time since Call Pickup (min)'][index] == min:\n",
    "            n += 1\n",
    "            if df1w4_sns['Outcome'][index] == 'Current ROSC':\n",
    "                rosc += 1\n",
    "            else:\n",
    "                norosc += 1\n",
    "        elif df1w4_sns['Time since Call Pickup (min)'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    nrosc.append(rosc)\n",
    "    nnorosc.append(norosc)\n",
    "ns_df1w4 = pd.DataFrame(list(range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df1w4['n'] = ns\n",
    "ns_df1w4['n - rosc'] = nrosc\n",
    "ns_df1w4['n - no rosc'] = nnorosc\n",
    "ns_df1w4\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(12,4)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=.8)\n",
    "sns.boxplot()\n",
    "palette = ['darkturquoise', 'lightgray']\n",
    "ax = sns.boxplot(data=df1w4_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue_order=['Current ROSC', 'Current No ROSC'], hue=\"Outcome\", whis=0, showfliers=False, palette=palette, linewidth=0.6)\n",
    "ax.set(ylim=(0, 100))\n",
    "ax.figure.savefig('figures/fig3.png',dpi=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf31739f-4a10-4685-b839-9b14d2bcc50b",
   "metadata": {},
   "source": [
    "### Calcs for paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b9316252-5bc2-4cda-ba93-e01d8cf8aa1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.5\n",
      "33.0\n"
     ]
    }
   ],
   "source": [
    "df1wrosc = df1w[df1w['ROSC'] == 1][['BOx@0min (pc)', 'BOx@1min (pc)', 'BOx@2min (pc)', 'BOx@3min (pc)', 'BOx@4min (pc)', 'BOx@5min (pc)',\n",
    " 'BOx@6min (pc)', 'BOx@7min (pc)', 'BOx@8min (pc)', 'BOx@9min (pc)', 'BOx@10min (pc)', 'BOx@11min (pc)', 'BOx@12min (pc)', 'BOx@13min (pc)',\n",
    " 'BOx@14min (pc)', 'BOx@15min (pc)', 'BOx@16min (pc)', 'BOx@17min (pc)', 'BOx@18min (pc)', 'BOx@19min (pc)', 'BOx@20min (pc)', 'BOx@21min (pc)',\n",
    " 'BOx@22min (pc)', 'BOx@23min (pc)', 'BOx@24min (pc)', 'BOx@25min (pc)', 'BOx@26min (pc)', 'BOx@27min (pc)', 'BOx@28min (pc)', 'BOx@29min (pc)',\n",
    " 'BOx@30min (pc)', 'BOx@31min (pc)', 'BOx@32min (pc)', 'BOx@33min (pc)', 'BOx@34min (pc)', 'BOx@35min (pc)', 'BOx@36min (pc)', 'BOx@37min (pc)',\n",
    " 'BOx@38min (pc)', 'BOx@39min (pc)', 'BOx@40min (pc)', 'BOx@41min (pc)', 'BOx@42min (pc)', 'BOx@43min (pc)', 'BOx@44min (pc)', 'BOx@45min (pc)',\n",
    " 'BOx@46min (pc)', 'BOx@47min (pc)', 'BOx@48min (pc)', 'BOx@49min (pc)', 'BOx@50min (pc)']].copy()\n",
    "df1wnorosc = df1w[df1w['ROSC'] == 0][['BOx@0min (pc)', 'BOx@1min (pc)', 'BOx@2min (pc)', 'BOx@3min (pc)', 'BOx@4min (pc)', 'BOx@5min (pc)',\n",
    " 'BOx@6min (pc)', 'BOx@7min (pc)', 'BOx@8min (pc)', 'BOx@9min (pc)', 'BOx@10min (pc)', 'BOx@11min (pc)', 'BOx@12min (pc)', 'BOx@13min (pc)',\n",
    " 'BOx@14min (pc)', 'BOx@15min (pc)', 'BOx@16min (pc)', 'BOx@17min (pc)', 'BOx@18min (pc)', 'BOx@19min (pc)', 'BOx@20min (pc)', 'BOx@21min (pc)',\n",
    " 'BOx@22min (pc)', 'BOx@23min (pc)', 'BOx@24min (pc)', 'BOx@25min (pc)', 'BOx@26min (pc)', 'BOx@27min (pc)', 'BOx@28min (pc)', 'BOx@29min (pc)',\n",
    " 'BOx@30min (pc)', 'BOx@31min (pc)', 'BOx@32min (pc)', 'BOx@33min (pc)', 'BOx@34min (pc)', 'BOx@35min (pc)', 'BOx@36min (pc)', 'BOx@37min (pc)',\n",
    " 'BOx@38min (pc)', 'BOx@39min (pc)', 'BOx@40min (pc)', 'BOx@41min (pc)', 'BOx@42min (pc)', 'BOx@43min (pc)', 'BOx@44min (pc)', 'BOx@45min (pc)',\n",
    " 'BOx@46min (pc)', 'BOx@47min (pc)', 'BOx@48min (pc)', 'BOx@49min (pc)', 'BOx@50min (pc)']].copy()\n",
    "\n",
    "roscmeds = []\n",
    "for col in df1wrosc.columns:\n",
    "    roscmeds.append(df1wrosc[col].median())\n",
    "\n",
    "noroscmeds = []\n",
    "for col in df1wnorosc.columns:\n",
    "    noroscmeds.append(df1wnorosc[col].median())\n",
    "\n",
    "roscmed = np.nanmedian(roscmeds)\n",
    "noroscmed = np.nanmedian(noroscmeds)\n",
    "print(roscmed)\n",
    "print(noroscmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3382b50c-059a-4262-bed9-b9346ec0dfff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.0\n",
      "62.0\n"
     ]
    }
   ],
   "source": [
    "print(np.nanquantile(roscmeds, 0.25))\n",
    "print(np.nanquantile(roscmeds, 0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2806784c-081a-4070-9bb3-c9482cee3de2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.5\n",
      "36.75\n"
     ]
    }
   ],
   "source": [
    "print(np.nanquantile(noroscmeds, 0.25))\n",
    "print(np.nanquantile(noroscmeds, 0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ceadbb7-1fb9-4278-8363-0ef5060a79f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a8732d-4bc6-4f1c-aa25-1e55218d2880",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4f9040f9-9a97-4731-b939-7d12299be45d",
   "metadata": {},
   "source": [
    "## Witnessed Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48047636-cf89-463b-a69e-7e45d3f6160d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Witnessed\n",
       "0    49\n",
       "1    44\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Witnessed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6cd84bd4-7501-4f17-a80f-6c350242500a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1w = df[df['Witnessed'] == 1][['BOx@0min (c)', 'BOx@1min (c)', 'BOx@2min (c)', 'BOx@3min (c)', 'BOx@4min (c)', 'BOx@5min (c)',\n",
    " 'BOx@6min (c)', 'BOx@7min (c)', 'BOx@8min (c)', 'BOx@9min (c)', 'BOx@10min (c)', 'BOx@11min (c)', 'BOx@12min (c)', 'BOx@13min (c)',\n",
    " 'BOx@14min (c)', 'BOx@15min (c)', 'BOx@16min (c)', 'BOx@17min (c)', 'BOx@18min (c)', 'BOx@19min (c)', 'BOx@20min (c)', 'BOx@21min (c)',\n",
    " 'BOx@22min (c)', 'BOx@23min (c)', 'BOx@24min (c)', 'BOx@25min (c)', 'BOx@26min (c)', 'BOx@27min (c)', 'BOx@28min (c)', 'BOx@29min (c)',\n",
    " 'BOx@30min (c)', 'BOx@31min (c)', 'BOx@32min (c)', 'BOx@33min (c)', 'BOx@34min (c)', 'BOx@35min (c)', 'BOx@36min (c)', 'BOx@37min (c)',\n",
    " 'BOx@38min (c)', 'BOx@39min (c)', 'BOx@40min (c)', 'BOx@41min (c)', 'BOx@42min (c)', 'BOx@43min (c)', 'BOx@44min (c)', 'BOx@45min (c)',\n",
    " 'BOx@46min (c)', 'BOx@47min (c)', 'BOx@48min (c)', 'BOx@49min (c)', 'BOx@50min (c)', 'ROSC']].copy()\n",
    "df1w.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24c7a94c-128c-47e5-8b6a-119a7aad4371",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make df for Seaborn plotting\n",
    "cols = df1w.columns[:-1]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, cassyes, cassno = [], [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno = 0, 0, 0\n",
    "    cyes, cno = [], []\n",
    "    for i in range(len(df1w)):\n",
    "        if np.isnan(df1w[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1w[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1w['ROSC'][i] == 0:\n",
    "                roscs.append('no')\n",
    "                rno += 1\n",
    "                cno.append(df['CASSID'][i])\n",
    "            else:\n",
    "                roscs.append('yes')\n",
    "                ryes += 1\n",
    "                cyes.append(df['CASSID'][i])\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    cassyes.append(cyes)\n",
    "    cassno.append(cno)\n",
    "    \n",
    "df1w_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1w_sns['rSO2 (%)'] = vals\n",
    "df1w_sns['ROSC'] = roscs\n",
    "\n",
    "df1w_sns\n",
    "\n",
    "ns_df1w = pd.DataFrame(df1w.columns[:-1], columns=['Time since ROSC (min)'])\n",
    "ns_df1w['n'] = ns\n",
    "ns_df1w['n - rosc'] = roscyes\n",
    "ns_df1w['n - no rosc'] = roscno\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df1w_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ffc4e77-2d1e-4028-b8eb-fc8cccaad0c1",
   "metadata": {},
   "source": [
    "### Drop bars where n < 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8cb7979a-5679-4f25-93ea-cb483a8ad8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time since Call Pickup (min)', ylabel='rSO2 (%)'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop the bars where n < 4\n",
    "roscdelis = []  # rosc indices/minute since Call Pickup where n < 4\n",
    "noroscdelis = []  # No-rosc indices/minute since Call Pickup where n < 4\n",
    "for i in range(len(ns_df1w)):\n",
    "    if ns_df1w['n - rosc'][i] < 4:\n",
    "        roscdelis.append(i)\n",
    "    if ns_df1w['n - no rosc'][i] < 4:\n",
    "        noroscdelis.append(i)\n",
    "# indices to delete in df1w_sns\n",
    "inds = []\n",
    "for i in range(len(df1w_sns)):\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in roscdelis and df1w_sns['ROSC'][i] == 'yes':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in noroscdelis and df1w_sns['ROSC'][i] == 'no':\n",
    "        inds.append(i)\n",
    "\n",
    "df1w4_sns = df1w_sns.copy()\n",
    "df1w4_sns.drop(inds, inplace=True)\n",
    "df1w4_sns.reset_index(inplace=True, drop=True)\n",
    "df1w4_sns\n",
    "\n",
    "# Get ns_df1w4 from df1w4_sns\n",
    "ns = []\n",
    "nrosc = []\n",
    "nnorosc = []\n",
    "for min in range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    rosc = 0\n",
    "    norosc = 0\n",
    "    for index, row in df1w4_sns.iterrows():\n",
    "        if df1w4_sns['Time since Call Pickup (min)'][index] == min:\n",
    "            n += 1\n",
    "            if df1w4_sns['ROSC'][index] == 'yes':\n",
    "                rosc += 1\n",
    "            else:\n",
    "                norosc += 1\n",
    "        elif df1w4_sns['Time since Call Pickup (min)'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    nrosc.append(rosc)\n",
    "    nnorosc.append(norosc)\n",
    "ns_df1w4 = pd.DataFrame(list(range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df1w4['n'] = ns\n",
    "ns_df1w4['n - rosc'] = nrosc\n",
    "ns_df1w4['n - no rosc'] = nnorosc\n",
    "ns_df1w4\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.boxplot()\n",
    "palette = ['b', 'peru']\n",
    "sns.boxplot(data=df1w4_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\", palette=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18ca6dd-205d-4599-9d22-76dfd4fe6629",
   "metadata": {},
   "source": [
    "### Post-ROSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70ddce65-77cf-4971-ab26-bb50c697f87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time since Call Pickup (min)', ylabel='rSO2 (%)'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1w = df[df['Witnessed'] == 1][['BOx@0min (pc)', 'BOx@1min (pc)', 'BOx@2min (pc)', 'BOx@3min (pc)', 'BOx@4min (pc)', 'BOx@5min (pc)',\n",
    " 'BOx@6min (pc)', 'BOx@7min (pc)', 'BOx@8min (pc)', 'BOx@9min (pc)', 'BOx@10min (pc)', 'BOx@11min (pc)', 'BOx@12min (pc)', 'BOx@13min (pc)',\n",
    " 'BOx@14min (pc)', 'BOx@15min (pc)', 'BOx@16min (pc)', 'BOx@17min (pc)', 'BOx@18min (pc)', 'BOx@19min (pc)', 'BOx@20min (pc)', 'BOx@21min (pc)',\n",
    " 'BOx@22min (pc)', 'BOx@23min (pc)', 'BOx@24min (pc)', 'BOx@25min (pc)', 'BOx@26min (pc)', 'BOx@27min (pc)', 'BOx@28min (pc)', 'BOx@29min (pc)',\n",
    " 'BOx@30min (pc)', 'BOx@31min (pc)', 'BOx@32min (pc)', 'BOx@33min (pc)', 'BOx@34min (pc)', 'BOx@35min (pc)', 'BOx@36min (pc)', 'BOx@37min (pc)',\n",
    " 'BOx@38min (pc)', 'BOx@39min (pc)', 'BOx@40min (pc)', 'BOx@41min (pc)', 'BOx@42min (pc)', 'BOx@43min (pc)', 'BOx@44min (pc)', 'BOx@45min (pc)',\n",
    " 'BOx@46min (pc)', 'BOx@47min (pc)', 'BOx@48min (pc)', 'BOx@49min (pc)', 'BOx@50min (pc)', 'ROSC']].copy()\n",
    "df1w.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df1w.columns[:-1]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, cassyes, cassno = [], [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno = 0, 0, 0\n",
    "    cyes, cno = [], []\n",
    "    for i in range(len(df1w)):\n",
    "        if np.isnan(df1w[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1w[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1w['ROSC'][i] == 0:\n",
    "                roscs.append('no')\n",
    "                rno += 1\n",
    "                cno.append(df['CASSID'][i])\n",
    "            else:\n",
    "                roscs.append('yes')\n",
    "                ryes += 1\n",
    "                cyes.append(df['CASSID'][i])\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    cassyes.append(cyes)\n",
    "    cassno.append(cno)\n",
    "    \n",
    "df1w_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1w_sns['rSO2 (%)'] = vals\n",
    "df1w_sns['ROSC'] = roscs\n",
    "\n",
    "df1w_sns\n",
    "\n",
    "ns_df1w = pd.DataFrame(df1w.columns[:-1], columns=['Time since ROSC (min)'])\n",
    "ns_df1w['n'] = ns\n",
    "ns_df1w['n - rosc'] = roscyes\n",
    "ns_df1w['n - no rosc'] = roscno\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df1w_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\")\n",
    "\n",
    "# Drop the bars where n < 4\n",
    "roscdelis = []  # rosc indices/minute since Call Pickup where n < 4\n",
    "noroscdelis = []  # No-rosc indices/minute since Call Pickup where n < 4\n",
    "for i in range(len(ns_df1w)):\n",
    "    if ns_df1w['n - rosc'][i] < 4:\n",
    "        roscdelis.append(i)\n",
    "    if ns_df1w['n - no rosc'][i] < 4:\n",
    "        noroscdelis.append(i)\n",
    "# indices to delete in df1w_sns\n",
    "inds = []\n",
    "for i in range(len(df1w_sns)):\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in roscdelis and df1w_sns['ROSC'][i] == 'yes':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in noroscdelis and df1w_sns['ROSC'][i] == 'no':\n",
    "        inds.append(i)\n",
    "\n",
    "df1w4_sns = df1w_sns.copy()\n",
    "df1w4_sns.drop(inds, inplace=True)\n",
    "df1w4_sns.reset_index(inplace=True, drop=True)\n",
    "df1w4_sns\n",
    "\n",
    "# Get ns_df1w4 from df1w4_sns\n",
    "ns = []\n",
    "nrosc = []\n",
    "nnorosc = []\n",
    "for min in range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    rosc = 0\n",
    "    norosc = 0\n",
    "    for index, row in df1w4_sns.iterrows():\n",
    "        if df1w4_sns['Time since Call Pickup (min)'][index] == min:\n",
    "            n += 1\n",
    "            if df1w4_sns['ROSC'][index] == 'yes':\n",
    "                rosc += 1\n",
    "            else:\n",
    "                norosc += 1\n",
    "        elif df1w4_sns['Time since Call Pickup (min)'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    nrosc.append(rosc)\n",
    "    nnorosc.append(norosc)\n",
    "ns_df1w4 = pd.DataFrame(list(range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df1w4['n'] = ns\n",
    "ns_df1w4['n - rosc'] = nrosc\n",
    "ns_df1w4['n - no rosc'] = nnorosc\n",
    "ns_df1w4\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.boxplot()\n",
    "palette = ['mediumaquamarine', 'peru']\n",
    "sns.boxplot(data=df1w4_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\", palette=palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "99d99cd2-ca31-4114-9f9e-45169e5578bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Witnessed\n",
       "0    49\n",
       "1    44\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Witnessed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8acbe96-ef73-4c99-aca0-33b0ce80152a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time since Call Pickup (min)</th>\n",
       "      <th>n</th>\n",
       "      <th>n - rosc</th>\n",
       "      <th>n - no rosc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>44</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>45</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>46</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>48</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Time since Call Pickup (min)   n  n - rosc  n - no rosc\n",
       "0                             17   4         4            0\n",
       "1                             18   8         4            4\n",
       "2                             19   9         5            4\n",
       "3                             20  12         7            5\n",
       "4                             21  14         7            7\n",
       "5                             22  18        11            7\n",
       "6                             23  21        14            7\n",
       "7                             24  26        19            7\n",
       "8                             25  29        22            7\n",
       "9                             26  31        23            8\n",
       "10                            27  31        22            9\n",
       "11                            28  31        21           10\n",
       "12                            29  30        20           10\n",
       "13                            30  30        20           10\n",
       "14                            31  28        18           10\n",
       "15                            32  29        19           10\n",
       "16                            33  29        19           10\n",
       "17                            34  29        19           10\n",
       "18                            35  29        19           10\n",
       "19                            36  28        18           10\n",
       "20                            37  27        17           10\n",
       "21                            38  26        17            9\n",
       "22                            39  26        17            9\n",
       "23                            40  25        16            9\n",
       "24                            41  25        16            9\n",
       "25                            42  25        16            9\n",
       "26                            43  23        15            8\n",
       "27                            44  22        15            7\n",
       "28                            45  22        15            7\n",
       "29                            46  20        14            6\n",
       "30                            47  19        13            6\n",
       "31                            48  19        13            6\n",
       "32                            49  12        12            0\n",
       "33                            50   9         9            0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ns_df1w4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a892910-a1dc-4e87-9b0e-1baad663a05b",
   "metadata": {},
   "source": [
    "## Unwitnessed Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bb08919b-2ccb-4cc7-b5f5-b45e648779af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time since Call Pickup (min)', ylabel='rSO2 (%)'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df1w = df[df['Witnessed'] == 0][['BOx@0min (c)', 'BOx@1min (c)', 'BOx@2min (c)', 'BOx@3min (c)', 'BOx@4min (c)', 'BOx@5min (c)',\n",
    " 'BOx@6min (c)', 'BOx@7min (c)', 'BOx@8min (c)', 'BOx@9min (c)', 'BOx@10min (c)', 'BOx@11min (c)', 'BOx@12min (c)', 'BOx@13min (c)',\n",
    " 'BOx@14min (c)', 'BOx@15min (c)', 'BOx@16min (c)', 'BOx@17min (c)', 'BOx@18min (c)', 'BOx@19min (c)', 'BOx@20min (c)', 'BOx@21min (c)',\n",
    " 'BOx@22min (c)', 'BOx@23min (c)', 'BOx@24min (c)', 'BOx@25min (c)', 'BOx@26min (c)', 'BOx@27min (c)', 'BOx@28min (c)', 'BOx@29min (c)',\n",
    " 'BOx@30min (c)', 'BOx@31min (c)', 'BOx@32min (c)', 'BOx@33min (c)', 'BOx@34min (c)', 'BOx@35min (c)', 'BOx@36min (c)', 'BOx@37min (c)',\n",
    " 'BOx@38min (c)', 'BOx@39min (c)', 'BOx@40min (c)', 'BOx@41min (c)', 'BOx@42min (c)', 'BOx@43min (c)', 'BOx@44min (c)', 'BOx@45min (c)',\n",
    " 'BOx@46min (c)', 'BOx@47min (c)', 'BOx@48min (c)', 'BOx@49min (c)', 'BOx@50min (c)', 'ROSC']].copy()\n",
    "df1w.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df1w.columns[:-1]\n",
    "times = []\n",
    "vals = []\n",
    "roscs = []\n",
    "ns, roscyes, roscno, cassyes, cassno = [], [], [], [], []\n",
    "for c in range(len(cols)):\n",
    "    n, ryes, rno = 0, 0, 0\n",
    "    cyes, cno = [], []\n",
    "    for i in range(len(df1w)):\n",
    "        if np.isnan(df1w[cols[c]][i]) == False:\n",
    "            times.append(c)\n",
    "            vals.append(df1w[cols[c]][i])\n",
    "            n += 1\n",
    "            if df1w['ROSC'][i] == 0:\n",
    "                roscs.append('no')\n",
    "                rno += 1\n",
    "                cno.append(df['CASSID'][i])\n",
    "            else:\n",
    "                roscs.append('yes')\n",
    "                ryes += 1\n",
    "                cyes.append(df['CASSID'][i])\n",
    "    ns.append(n)\n",
    "    roscyes.append(ryes)\n",
    "    roscno.append(rno)\n",
    "    cassyes.append(cyes)\n",
    "    cassno.append(cno)\n",
    "    \n",
    "df1w_sns = pd.DataFrame(times, columns=['Time since Call Pickup (min)'])\n",
    "df1w_sns['rSO2 (%)'] = vals\n",
    "df1w_sns['ROSC'] = roscs\n",
    "\n",
    "df1w_sns\n",
    "\n",
    "ns_df1w = pd.DataFrame(df1w.columns[:-1], columns=['Time since ROSC (min)'])\n",
    "ns_df1w['n'] = ns\n",
    "ns_df1w['n - rosc'] = roscyes\n",
    "ns_df1w['n - no rosc'] = roscno\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df1w_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\")\n",
    "\n",
    "# Drop the bars where n < 4\n",
    "roscdelis = []  # rosc indices/minute since Call Pickup where n < 4\n",
    "noroscdelis = []  # No-rosc indices/minute since Call Pickup where n < 4\n",
    "for i in range(len(ns_df1w)):\n",
    "    if ns_df1w['n - rosc'][i] < 4:\n",
    "        roscdelis.append(i)\n",
    "    if ns_df1w['n - no rosc'][i] < 4:\n",
    "        noroscdelis.append(i)\n",
    "# indices to delete in df1w_sns\n",
    "inds = []\n",
    "for i in range(len(df1w_sns)):\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in roscdelis and df1w_sns['ROSC'][i] == 'yes':\n",
    "        inds.append(i)\n",
    "    if df1w_sns['Time since Call Pickup (min)'][i] in noroscdelis and df1w_sns['ROSC'][i] == 'no':\n",
    "        inds.append(i)\n",
    "\n",
    "df1w4_sns = df1w_sns.copy()\n",
    "df1w4_sns.drop(inds, inplace=True)\n",
    "df1w4_sns.reset_index(inplace=True, drop=True)\n",
    "df1w4_sns\n",
    "\n",
    "# Get ns_df1w4 from df1w4_sns\n",
    "ns = []\n",
    "nrosc = []\n",
    "nnorosc = []\n",
    "for min in range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    rosc = 0\n",
    "    norosc = 0\n",
    "    for index, row in df1w4_sns.iterrows():\n",
    "        if df1w4_sns['Time since Call Pickup (min)'][index] == min:\n",
    "            n += 1\n",
    "            if df1w4_sns['ROSC'][index] == 'yes':\n",
    "                rosc += 1\n",
    "            else:\n",
    "                norosc += 1\n",
    "        elif df1w4_sns['Time since Call Pickup (min)'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    nrosc.append(rosc)\n",
    "    nnorosc.append(norosc)\n",
    "ns_df1w4 = pd.DataFrame(list(range(df1w4_sns['Time since Call Pickup (min)'][0], df1w4_sns['Time since Call Pickup (min)'][len(df1w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df1w4['n'] = ns\n",
    "ns_df1w4['n - rosc'] = nrosc\n",
    "ns_df1w4['n - no rosc'] = nnorosc\n",
    "ns_df1w4\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.boxplot()\n",
    "palette = ['peru', 'b']\n",
    "sns.boxplot(data=df1w4_sns, x=\"Time since Call Pickup (min)\", y=\"rSO2 (%)\", hue=\"ROSC\", palette=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74ebfcd-9e25-4834-aeba-670e05b4ccb1",
   "metadata": {},
   "source": [
    "# 2 - FIGURE 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09ef2c42-034e-426a-9ef1-afecffa3a883",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(16,8)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=1.2)\n",
    "palette = ['lightgray', 'royalblue']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99dfa2db-31f4-411c-9a27-d049ccb63590",
   "metadata": {},
   "source": [
    "## All ROSC cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9ce565b8-6481-49ba-90a2-3008d463656b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df2 = df[df['ROSC'] == 1][['BOx@-10min (r)', 'BOx@-9min (r)', 'BOx@-8min (r)', 'BOx@-7min (r)', 'BOx@-6min (r)', \n",
    "          'BOx@-5min (r)', 'BOx@-4min (r)', 'BOx@-3min (r)', 'BOx@-2min (r)', 'BOx@-1min (r)', \n",
    "          'BOx@rosc (r)', \n",
    "          'BOx@1min (r)', 'BOx@2min (r)', 'BOx@3min (r)', 'BOx@4min (r)', 'BOx@5min (r)', \n",
    "          'BOx@6min (r)', 'BOx@7min (r)', 'BOx@8min (r)', 'BOx@9min (r)', 'BOx@10min (r)',\n",
    "          'ROSC-Died', 'ROSC-Survived', 'ROSC-CPC12', 'CASSID']].copy()\n",
    "df2.reset_index(inplace=True, drop=True)\n",
    "df2\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df2.columns[:-4]\n",
    "patients = []\n",
    "times = []\n",
    "vals = []\n",
    "survived = []\n",
    "ns = []\n",
    "nsurvs = []\n",
    "ndieds = []\n",
    "for c in range(len(cols)):\n",
    "    n = 0\n",
    "    nsurv = 0\n",
    "    ndied = 0\n",
    "    for i in range(len(df2)):\n",
    "        if np.isnan(df2[cols[c]][i]) == False:\n",
    "            patients.append(df['CASSID'][i])\n",
    "            times.append(c - 10)\n",
    "            vals.append(df2[cols[c]][i])\n",
    "            if df2['ROSC-CPC12'][i] == 1:\n",
    "                survived.append('ROSC-CPC 1-2')\n",
    "                nsurv += 1\n",
    "            else:\n",
    "                survived.append('ROSC-Died/CPC 3-4')\n",
    "                ndied += 1\n",
    "            n += 1\n",
    "    ns.append(n)\n",
    "    nsurvs.append(nsurv)\n",
    "    ndieds.append(ndied)\n",
    "df2_sns = pd.DataFrame(times, columns=['Time since ROSC (min)'])\n",
    "df2_sns['time'] = times\n",
    "df2_sns['rSO2 (%)'] = vals\n",
    "df2_sns['b_ox'] = vals\n",
    "df2_sns['patient'] = patients\n",
    "df2_sns['Outcome'] = survived\n",
    "\n",
    "df2_sns\n",
    "\n",
    "ns_df = pd.DataFrame(df2.columns[:-4], columns=['Time since ROSC (min)'])\n",
    "ns_df['n'] = ns\n",
    "ns_df['n - ROSC-CPC 1or2'] = nsurvs\n",
    "ns_df['n - ROSC-Died'] = ndieds\n",
    "\n",
    "\n",
    "# Drop the bars where n < 4\n",
    "survdelis = []  # survivor indices/minute from ROSC where n < 4\n",
    "nonsurvdelis = []  # Nonsurvivor indices/minute from ROSC where n < 4\n",
    "for i in range(len(ns_df)):\n",
    "    if ns_df['n - ROSC-CPC 1or2'][i] < 4:\n",
    "        survdelis.append(i - 10)\n",
    "    if ns_df['n - ROSC-Died'][i] < 4:\n",
    "        nonsurvdelis.append(i - 10)\n",
    "# indices to delete in df2w_sns\n",
    "inds = []\n",
    "for i in range(len(df2_sns)):\n",
    "    if df2_sns['time'][i] in survdelis and df2_sns['Outcome'][i] == 'ROSC-CPC 1-2':\n",
    "        inds.append(i)\n",
    "    if df2_sns['time'][i] in nonsurvdelis and df2_sns['Outcome'][i] == 'ROSC-Died/CPC 3-4':\n",
    "        inds.append(i)\n",
    "\n",
    "df24_sns = df2_sns.copy()\n",
    "df24_sns.drop(inds, inplace=True)\n",
    "df24_sns.reset_index(inplace=True, drop=True)\n",
    "df24_sns\n",
    "\n",
    "\n",
    "# Get ns_df2w4 from df2w4_sns\n",
    "ns = []\n",
    "ncpc12 = []\n",
    "nnot = []\n",
    "for min in range(df24_sns['time'][0], df24_sns['time'][len(df24_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    cpc12 = 0\n",
    "    cpcnot = 0\n",
    "    for index, row in df24_sns.iterrows():\n",
    "        if df24_sns['time'][index] == min:\n",
    "            n += 1\n",
    "            if df24_sns['Outcome'][index] == 'ROSC-CPC 1-2':\n",
    "                cpc12 += 1\n",
    "            else:\n",
    "                cpcnot += 1\n",
    "        elif df24_sns['time'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    ncpc12.append(cpc12)\n",
    "    nnot.append(cpcnot)\n",
    "ns_df24 = pd.DataFrame(list(range(df24_sns['time'][0], df24_sns['time'][len(df24_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df24['n'] = ns\n",
    "ns_df24['n - survived CPC 1 or 2'] = ncpc12\n",
    "ns_df24['n - not'] = nnot\n",
    "ns_df24\n",
    "\n",
    "\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(8,5)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=.8)\n",
    "sns.boxplot()\n",
    "# palette = ['paleturquoise', 'seagreen']\n",
    "palette = ['gold', 'lightskyblue']\n",
    "ax = sns.boxplot(data=df24_sns, x=\"Time since ROSC (min)\", y=\"rSO2 (%)\", hue_order=['ROSC-CPC 1-2', 'ROSC-Died/CPC 3-4'], hue=\"Outcome\", whis=0, showfliers=False, palette=palette, linewidth=0.6)\n",
    "plt.xlim(7.5, 20.5)\n",
    "ax.set(ylim=(0, 100))\n",
    "ax.figure.savefig('figures/fig4.png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "10ed6f04-56ee-464b-9edf-1c2d3d9d6ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time since Call Pickup (min)</th>\n",
       "      <th>n</th>\n",
       "      <th>n - survived CPC 1 or 2</th>\n",
       "      <th>n - not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-10</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-9</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-8</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-7</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-5</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-4</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-3</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Time since Call Pickup (min)   n  n - survived CPC 1 or 2  n - not\n",
       "0                            -10   9                        0        9\n",
       "1                             -9  11                        0       11\n",
       "2                             -8  14                        0       14\n",
       "3                             -7  16                        0       16\n",
       "4                             -6  19                        0       19\n",
       "5                             -5  24                        0       24\n",
       "6                             -4  27                        0       27\n",
       "7                             -3  31                        0       31\n",
       "8                             -2  36                        4       32\n",
       "9                             -1  37                        4       33\n",
       "10                             0  43                        7       36\n",
       "11                             1  45                        7       38\n",
       "12                             2  46                        8       38\n",
       "13                             3  40                        8       32\n",
       "14                             4  36                        8       28\n",
       "15                             5  35                        8       27\n",
       "16                             6  33                        8       25\n",
       "17                             7  33                        8       25\n",
       "18                             8  31                        8       23\n",
       "19                             9  30                        8       22\n",
       "20                            10  27                        7       20"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ns_df24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2ca4c263-d75f-4dd2-9902-b90fa8aea649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the boxplot\n",
    "sns.set_theme(rc={'figure.figsize':(8,5)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=.8)\n",
    "sns.boxplot()\n",
    "palette = ['lightskyblue', 'gold']\n",
    "ax = sns.boxplot(data=df24_sns, x=\"Time since ROSC (min)\", y=\"rSO2 (%)\", hue=\"Outcome\", showfliers=False, whis=0, palette=palette)\n",
    "plt.xlim(7, 20.5)\n",
    "ax.set(ylim=(0, 100))\n",
    "ax.figure.savefig('figures/fig3_nowhisks.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95434f5c-478e-47f6-8cc5-af1f296d69b3",
   "metadata": {},
   "source": [
    "### Witnessed Only, includes cases with no pre-ROSC medians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1452acfe-cdff-4eb2-b5af-cdabdb77b567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.0, 20.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df2 = df[(df['ROSC'] == 1) & (df['Witnessed'] == 1)][['BOx@-10min (r)', 'BOx@-9min (r)', 'BOx@-8min (r)', 'BOx@-7min (r)', 'BOx@-6min (r)', \n",
    "          'BOx@-5min (r)', 'BOx@-4min (r)', 'BOx@-3min (r)', 'BOx@-2min (r)', 'BOx@-1min (r)', \n",
    "          'BOx@rosc (r)', \n",
    "          'BOx@1min (r)', 'BOx@2min (r)', 'BOx@3min (r)', 'BOx@4min (r)', 'BOx@5min (r)', \n",
    "          'BOx@6min (r)', 'BOx@7min (r)', 'BOx@8min (r)', 'BOx@9min (r)', 'BOx@10min (r)',\n",
    "          'ROSC-Died', 'ROSC-Survived', 'ROSC-CPC12', 'CASSID']].copy()\n",
    "df2.reset_index(inplace=True, drop=True)\n",
    "df2\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df2.columns[:-4]\n",
    "patients = []\n",
    "times = []\n",
    "vals = []\n",
    "survived = []\n",
    "ns = []\n",
    "nsurvs = []\n",
    "ndieds = []\n",
    "for c in range(len(cols)):\n",
    "    n = 0\n",
    "    nsurv = 0\n",
    "    ndied = 0\n",
    "    for i in range(len(df2)):\n",
    "        if np.isnan(df2[cols[c]][i]) == False:\n",
    "            patients.append(df['CASSID'][i])\n",
    "            times.append(c - 10)\n",
    "            vals.append(df2[cols[c]][i])\n",
    "            if df2['ROSC-CPC12'][i] == 1:\n",
    "                survived.append('yes')\n",
    "                nsurv += 1\n",
    "            else:\n",
    "                survived.append('no')\n",
    "                ndied += 1\n",
    "            n += 1\n",
    "    ns.append(n)\n",
    "    nsurvs.append(nsurv)\n",
    "    ndieds.append(ndied)\n",
    "df2_sns = pd.DataFrame(times, columns=['Time since ROSC (min)'])\n",
    "df2_sns['time'] = times\n",
    "df2_sns['rSO2 (%)'] = vals\n",
    "df2_sns['b_ox'] = vals\n",
    "df2_sns['patient'] = patients\n",
    "df2_sns['ROSC-Survived-CPC-1or2'] = survived\n",
    "\n",
    "df2_sns\n",
    "\n",
    "ns_df = pd.DataFrame(df2.columns[:-4], columns=['Time since ROSC (min)'])\n",
    "ns_df['n'] = ns\n",
    "ns_df['n - ROSC-CPC 1or2'] = nsurvs\n",
    "ns_df['n - ROSC-died'] = ndieds\n",
    "\n",
    "\n",
    "# Drop the bars where n < 4\n",
    "survdelis = []  # survivor indices/minute from ROSC where n < 4\n",
    "nonsurvdelis = []  # Nonsurvivor indices/minute from ROSC where n < 4\n",
    "for i in range(len(ns_df)):\n",
    "    if ns_df['n - ROSC-CPC 1or2'][i] < 4:\n",
    "        survdelis.append(i - 10)\n",
    "    if ns_df['n - ROSC-died'][i] < 4:\n",
    "        nonsurvdelis.append(i - 10)\n",
    "# indices to delete in df2w_sns\n",
    "inds = []\n",
    "for i in range(len(df2_sns)):\n",
    "    if df2_sns['time'][i] in survdelis and df2_sns['ROSC-Survived-CPC-1or2'][i] == 'yes':\n",
    "        inds.append(i)\n",
    "    if df2_sns['time'][i] in nonsurvdelis and df2_sns['ROSC-Survived-CPC-1or2'][i] == 'no':\n",
    "        inds.append(i)\n",
    "\n",
    "df24_sns = df2_sns.copy()\n",
    "df24_sns.drop(inds, inplace=True)\n",
    "df24_sns.reset_index(inplace=True, drop=True)\n",
    "df24_sns\n",
    "\n",
    "\n",
    "# Get ns_df2w4 from df2w4_sns\n",
    "ns = []\n",
    "ncpc12 = []\n",
    "nnot = []\n",
    "for min in range(df24_sns['time'][0], df24_sns['time'][len(df24_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    cpc12 = 0\n",
    "    cpcnot = 0\n",
    "    for index, row in df24_sns.iterrows():\n",
    "        if df24_sns['time'][index] == min:\n",
    "            n += 1\n",
    "            if df24_sns['ROSC-Survived-CPC-1or2'][index] == 'yes':\n",
    "                cpc12 += 1\n",
    "            else:\n",
    "                cpcnot += 1\n",
    "        elif df24_sns['time'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    ncpc12.append(cpc12)\n",
    "    nnot.append(cpcnot)\n",
    "ns_df24 = pd.DataFrame(list(range(df24_sns['time'][0], df24_sns['time'][len(df24_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df24['n'] = ns\n",
    "ns_df24['n - survived CPC 1 or 2'] = ncpc12\n",
    "ns_df24['n - not'] = nnot\n",
    "ns_df24\n",
    "\n",
    "\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.boxplot()\n",
    "sns.boxplot(data=df24_sns, x=\"Time since ROSC (min)\", y=\"rSO2 (%)\", hue=\"ROSC-Survived-CPC-1or2\", palette=palette)\n",
    "plt.xlim(6, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b1c22b32-949e-4ff3-911e-1b89ae812f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "ns_df24.to_csv('deleteLater.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd554d3-c154-49c4-b9d3-3cd524405b6f",
   "metadata": {},
   "source": [
    "## Only cases with BOTH pre- AND post-ROSC values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "97394324-3c9d-4635-9fbc-97e840691ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df2w = df[(df['ROSC'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)][['BOx@-10min (r)', 'BOx@-9min (r)', 'BOx@-8min (r)', 'BOx@-7min (r)', 'BOx@-6min (r)', \n",
    "          'BOx@-5min (r)', 'BOx@-4min (r)', 'BOx@-3min (r)', 'BOx@-2min (r)', 'BOx@-1min (r)', \n",
    "          'BOx@rosc (r)', \n",
    "          'BOx@1min (r)', 'BOx@2min (r)', 'BOx@3min (r)', 'BOx@4min (r)', 'BOx@5min (r)', \n",
    "          'BOx@6min (r)', 'BOx@7min (r)', 'BOx@8min (r)', 'BOx@9min (r)', 'BOx@10min (r)',\n",
    "          'ROSC-Died', 'ROSC-Survived', 'ROSC-CPC12', 'CASSID']].copy()\n",
    "df2w.reset_index(inplace=True, drop=True)\n",
    "df2w\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df2w.columns[:-4]\n",
    "patients = []\n",
    "times = []\n",
    "vals = []\n",
    "survived = []\n",
    "ns = []\n",
    "nsurvs = []\n",
    "ndieds = []\n",
    "for c in range(len(cols)):\n",
    "    n = 0\n",
    "    nsurv = 0\n",
    "    ndied = 0\n",
    "    for i in range(len(df2w)):\n",
    "        if np.isnan(df2w[cols[c]][i]) == False:\n",
    "            patients.append(df['CASSID'][i])\n",
    "            times.append(c - 10)\n",
    "            vals.append(df2w[cols[c]][i])\n",
    "            if df2w['ROSC-CPC12'][i] == 1:\n",
    "                survived.append('yes')\n",
    "                nsurv += 1\n",
    "            else:\n",
    "                survived.append('no')\n",
    "                ndied += 1\n",
    "            n += 1\n",
    "    ns.append(n)\n",
    "    nsurvs.append(nsurv)\n",
    "    ndieds.append(ndied)\n",
    "df2w_sns = pd.DataFrame(times, columns=['Time since ROSC (min)'])\n",
    "df2w_sns['time'] = times\n",
    "df2w_sns['rSO2 (%)'] = vals\n",
    "df2w_sns['b_ox'] = vals\n",
    "df2w_sns['patient'] = patients\n",
    "df2w_sns['ROSC-Survived-CPC-1or2'] = survived\n",
    "\n",
    "df2w_sns\n",
    "\n",
    "ns_dfw = pd.DataFrame(df2w.columns[:-4], columns=['Time since ROSC (min)'])\n",
    "ns_dfw['n'] = ns\n",
    "ns_dfw['n - ROSC-CPC 1or2'] = nsurvs\n",
    "ns_dfw['n - ROSC-died'] = ndieds\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df2w_sns, x=\"Time since ROSC (min)\", y=\"rSO2 (%)\", hue=\"ROSC-Survived-CPC-1or2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daeb2ff0-ba17-4a90-9e49-d32fed6e29d8",
   "metadata": {},
   "source": [
    "### Drop bars where n < 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5efc21df-94e0-41d9-9d7e-66b72d4bb4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.0, 20.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop the bars where n < 4\n",
    "survdelis = []  # survivor indices/minute from ROSC where n < 4\n",
    "nonsurvdelis = []  # Nonsurvivor indices/minute from ROSC where n < 4\n",
    "for i in range(len(ns_dfw)):\n",
    "    if ns_dfw['n - ROSC-CPC 1or2'][i] < 4:\n",
    "        survdelis.append(i - 10)\n",
    "    if ns_dfw['n - ROSC-died'][i] < 4:\n",
    "        nonsurvdelis.append(i - 10)\n",
    "# indices to delete in df2w_sns\n",
    "inds = []\n",
    "for i in range(len(df2w_sns)):\n",
    "    if df2w_sns['time'][i] in survdelis and df2w_sns['ROSC-Survived-CPC-1or2'][i] == 'yes':\n",
    "        inds.append(i)\n",
    "    if df2w_sns['time'][i] in nonsurvdelis and df2w_sns['ROSC-Survived-CPC-1or2'][i] == 'no':\n",
    "        inds.append(i)\n",
    "\n",
    "df2w4_sns = df2w_sns.copy()\n",
    "df2w4_sns.drop(inds, inplace=True)\n",
    "df2w4_sns.reset_index(inplace=True, drop=True)\n",
    "df2w4_sns\n",
    "\n",
    "\n",
    "# Get ns_df2w4 from df2w4_sns\n",
    "ns = []\n",
    "ncpc12 = []\n",
    "nnot = []\n",
    "for min in range(df2w4_sns['time'][0], df2w4_sns['time'][len(df2w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    cpc12 = 0\n",
    "    cpcnot = 0\n",
    "    for index, row in df2w4_sns.iterrows():\n",
    "        if df2w4_sns['time'][index] == min:\n",
    "            n += 1\n",
    "            if df2w4_sns['ROSC-Survived-CPC-1or2'][index] == 'yes':\n",
    "                cpc12 += 1\n",
    "            else:\n",
    "                cpcnot += 1\n",
    "        elif df2w4_sns['time'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    ncpc12.append(cpc12)\n",
    "    nnot.append(cpcnot)\n",
    "ns_df2w4 = pd.DataFrame(list(range(df2w4_sns['time'][0], df2w4_sns['time'][len(df2w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df2w4['n'] = ns\n",
    "ns_df2w4['n - survived CPC 1 or 2'] = ncpc12\n",
    "ns_df2w4['n - not'] = nnot\n",
    "ns_df2w4\n",
    "\n",
    "\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.boxplot()\n",
    "sns.boxplot(data=df2w4_sns, x=\"Time since ROSC (min)\", y=\"rSO2 (%)\", hue=\"ROSC-Survived-CPC-1or2\", palette=palette)\n",
    "plt.xlim(6, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c8cca2-82e4-4ad2-a208-f07da285dac7",
   "metadata": {},
   "source": [
    "## Witnessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "afcd9e1a-6c2f-4514-a0e9-b0a485b7d25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make df with only columns I want to plot\n",
    "df2w = df[(df['ROSC'] == 1) & (df['Witnessed'] == 1)][['BOx@-10min (r)', 'BOx@-9min (r)', 'BOx@-8min (r)', 'BOx@-7min (r)', 'BOx@-6min (r)', \n",
    "          'BOx@-5min (r)', 'BOx@-4min (r)', 'BOx@-3min (r)', 'BOx@-2min (r)', 'BOx@-1min (r)', \n",
    "          'BOx@rosc (r)', \n",
    "          'BOx@1min (r)', 'BOx@2min (r)', 'BOx@3min (r)', 'BOx@4min (r)', 'BOx@5min (r)', \n",
    "          'BOx@6min (r)', 'BOx@7min (r)', 'BOx@8min (r)', 'BOx@9min (r)', 'BOx@10min (r)',\n",
    "          'ROSC-Died', 'ROSC-Survived', 'ROSC-CPC12', 'CASSID']].copy()\n",
    "df2w.reset_index(inplace=True, drop=True)\n",
    "df2w\n",
    "\n",
    "#Make df for Seaborn plotting\n",
    "cols = df2w.columns[:-4]\n",
    "patients = []\n",
    "times = []\n",
    "vals = []\n",
    "survived = []\n",
    "ns = []\n",
    "nsurvs = []\n",
    "ndieds = []\n",
    "for c in range(len(cols)):\n",
    "    n = 0\n",
    "    nsurv = 0\n",
    "    ndied = 0\n",
    "    for i in range(len(df2w)):\n",
    "        if np.isnan(df2w[cols[c]][i]) == False:\n",
    "            patients.append(df['CASSID'][i])\n",
    "            times.append(c - 10)\n",
    "            vals.append(df2w[cols[c]][i])\n",
    "            if df2w['ROSC-CPC12'][i] == 1:\n",
    "                survived.append('yes')\n",
    "                nsurv += 1\n",
    "            else:\n",
    "                survived.append('no')\n",
    "                ndied += 1\n",
    "            n += 1\n",
    "    ns.append(n)\n",
    "    nsurvs.append(nsurv)\n",
    "    ndieds.append(ndied)\n",
    "df2w_sns = pd.DataFrame(times, columns=['Time since ROSC (min)'])\n",
    "df2w_sns['time'] = times\n",
    "df2w_sns['rSO2 (%)'] = vals\n",
    "df2w_sns['b_ox'] = vals\n",
    "df2w_sns['patient'] = patients\n",
    "df2w_sns['ROSC-Survived-CPC-1or2'] = survived\n",
    "\n",
    "df2w_sns\n",
    "\n",
    "ns_dfw = pd.DataFrame(df2w.columns[:-4], columns=['Time since ROSC (min)'])\n",
    "ns_dfw['n'] = ns\n",
    "ns_dfw['n - ROSC-CPC 1or2'] = nsurvs\n",
    "ns_dfw['n - ROSC-died'] = ndieds\n",
    "\n",
    "# #Plot the boxplot\n",
    "# sns.boxplot()\n",
    "# sns.boxplot(data=df2w_sns, x=\"Time since ROSC (min)\", y=\"rSO2 (%)\", hue=\"ROSC-Survived-CPC-1or2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06e9cb4-125a-4177-9801-3ee1d3570923",
   "metadata": {},
   "source": [
    "### Drop bars where n < 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "69ea1bf8-12b7-4534-94fc-f8517497533f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time since ROSC (min)', ylabel='rSO2 (%)'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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15OTkaDYxM1YKhYIvvviCAQMG0KBBA02Zt3qpqpKSEnJycqhfv75Jba6oS9OmTTl58qShw9Ab9ffPysbBYFqb76mpVCqSkpKwtrY22kkYVfnvf//LzZs36d+/v1a7mZkZ/fr148CBA2RlZZnM5lf9+/fnlVde4eTJk1y9epUWLVrQpEkTZs6cCWDSyx41aNCAOnXqVLp04Pnz57G1tTXIRoxPxie+ATRq1IhVq1ZptT3Mh696DYSMjIwKx9Rtzz///ENEWPOq8xjY2dlpLX6/ZcsWVCqVZrdVY1FZXwsLCykrK6t0ZuitW7dQKpW1EaLeVPe1rV6rxZhLAirrq5mZGZ6enuzZs0fnF+X8/HwsLCyMbkbTw7xv/f333wBG94tqZX399ddfAZgxY4bO26kHLf/3f/+Hs7NzzQapJw/zvJaXl6NUKo3uC8b9+lpcXMyECRM4fvw4ixcv1uwabIz0Nc4wNs8//7xmZpZ6Yzq1zMxMAJNdP+pJER0dTXR0NP369XuiEpRq6hnBprDDd3FxMQqFgpSUFFJSUioc37x5M5s3b2bKlClaM5lMze3bt8nJyTG6sVJVOnbsyNq1a7V+0FVTt5niZle7d+/m3Llz+Pv7m+RnrvrHPnW5891u3boFYHQVj/fz1FNP4erqqvl/WVkZ6enptGrVilatWhkusBpmZmaGh4cH+/btIzc3V2vznJs3b3L06FE8PDwwMzOr9dierE/9WmRtba1VvvGwWrRogbe3N/v27ePIkSN4eHgAd94kYmNjqVu3boWZFI+LB30Mrly5QmRkJI6Ojka3CHFlfb19+zYODg789ddfZGVlac0QzcrK4tSpU3Tt2rUWI3101Xlei4uLNUmcF154oZYi07+q+hoQEMCePXuIjY3VWl/o559/5sKFC7z00ktaZeHGoKr+Xrp0qcLgurS0VLNUQe/evWs8Pn2qrK916tThueeeq9D+448/snnzZt58803atm2rc+bA46qq5/XixYs6+7J27VrKysqMblZ7VX0tLi5m/PjxHD9+nMjISPr27VvL0emXvsYZxqZPnz4sXLiQxMREgoODNaVYBQUF/PTTT3Tq1Mlk13l+EkRHRxMVFcWAAQNYvHixySYodX2mAuTk5LB161bs7OxMYgbP008/zZIlSyq0X758mQ8++IBu3boxevRok9lEsrLndfny5RQVFfHaa68ZIKqa4efnR/369dm4cSNTpkzBzs4OgGvXrrF+/XosLS3p3r27gaPUv8TERACTLPWGO5V+O3fuJDk5WWsilFKpZNOmTZibm5v8D4FffvklRUVFvPvuu4YOpcb5+/uzb98+Vq1aRUREhKZ93bp1lJSUGOw9yzQ/+R9zCoVCU46l3pjgzz//ZOnSpcCdRbTv/sIfERHBmDFjCAkJITg4GAcHB3744QcOHz7MvHnzDDIF91GlpKSwfv16unbtiqOjIzk5OSQlJXH9+nX+85//4ODgYOgQ9cLc3JywsDDmzZvH+PHjGTFiBK1ateLMmTPEx8djYWGhmZllSjZt2sTNmzcZOnSoQX59qQ3+/v788MMPrF27lsLCQl544QXOnj3LmjVrsLOzY+7cuYYOUa8GDx6Mj48P7u7umt29N2zYQF5eHiEhIbi5uRk6RL1o27atzi+Gf/75JwCdOnUyyvULKzN48GC8vLzw8PCgcePGFBcXs3fvXtLS0mjWrJlJ7TA7fvx4/vjjDwYOHEhpaSkbN27UOu7q6kr79u0NFJ1+Peg4w5jY29vzzjvv8MEHHzBy5EiGDx+OUqkkNjYWlUqlNcg2Ber3WYDc3FxUKpXmeQSYOnWqoULTuzVr1hAVFcUzzzxDz549+fHHH7WO161b12RKDL/++mt27dpFjx49NCXdp0+fZsOGDZSUlLBo0SKj+6FTlzp16uh8ztRl302bNjWZ5xTuPK/p6em8/PLLNG3alBs3bvD777+zY8cO2rRpw7Rp0wwdot7Y2dkRHh7OO++8w9ChQwkMDMTMzIykpCQuXLjArFmzTG7zwcLCQn755RfatGljUmPBu40bN46NGzeydu1aCgoKeOmllygtLWXTpk1kZ2cTFBRE06ZNDR2m3vTv3x8/Pz+cnZ0pLS0lNTWVvXv3MmrUKF5//XVDh/dQHmTcMGTIEDZs2EBsbCwKhQIfHx+ys7OJi4vDx8enwt4LtcVMpVKpDHLPT7CcnJwqZz8GBASwaNEirbZjx44RGRnJgQMHUCqVuLi4EBISUmG9CGNx5MgRFi9ezPHjx7l69SoODg50796dKVOmGE0J5YNITU0lNjaWo0ePcv36dezt7fHx8eEf//iHySR37vbaa69x6tQp0tLSTKq05V43b97km2++YdOmTeTl5VG3bl26devGjBkzTGZWgNqiRYvYu3cvubm5XLt2jbp16+Lu7s6IESPo16+focOrcVFRUURHR7NmzRqTGpguWbKEXbt2cebMGYqKirCyssLZ2ZlevXoRHBxslD+CVebuUh5dpk+fTlhYWC1FU7MeZpxhbLZs2UJMTAzHjx/H0tKSTp06MWvWLJNJNKsFBQVVuXZ1dnZ2LUZTs+bOncv69esrPd6sWTO2bdtWixHVnF27dhEfH8+RI0coLCzk1q1bODk54eXlxbhx4x7bpZz0Rf0eNXToUBYuXGjocPRm69atxMXFceLECa5cuYK5uTktW7bEz8+P0NBQk9yEIy0tjW+++YY//viD27dv4+LiQnBwsFGtZ11dq1atYtGiRcydO5fx48cbOpwak5OTw9KlS9m1axcXL17E0tKSdu3aMWzYME0y2lS88847HDhwgAsXLmBpaYmbmxujRo0y6uWAHnTccP36dZYsWcJPP/2kqbAaMGAA06ZNo27dujUdrk6SpBRCCCGEEEIIIYQQQhiUuaEDEEIIIYQQQgghhBBCPNkkSSmEEEIIIYQQQgghhDAoSVIKIYQQQgghhBBCCCEMSpKUQgghhBBCCCGEEEIIg5IkpRBCCCGEEEIIIYQQwqAkSSmEEEIIIYQQQgghhDAoSVIKIYQQQgghhBBCCCEMSpKUQgghhBBCCCGEEEIIg5IkpRBCCCGEEEIIIYQQwqAkSSmEEEIIUQ3Jycm4urqSnJxs6FD0KicnB1dXV+bOnWvoUMRDKisro2/fvkyePLlG76d379707t37ka8zZcoU+vTpQ1lZmR6iEkIIIYSpsDB0AEIIIYQQtc3V1fWBzv/0009rKBKhD/c+n+bm5tSvXx8XFxfeeOMN/P39MTMzq/T2u3btIiEhgaysLC5fvoyNjQ0tW7akV69eBAUFYW9vr/N258+fJyYmhp07d5Kbm4tKpcLR0ZHmzZvTqVMn3njjDVq2bFnhdiUlJSQkJLBt2zZOnDiBQqHAxsaGVq1a8eKLLzJ06FBatGhR7f7HxsZy9uxZvvrqq2rfxpD++c9/8vrrrxMbG0tISIihwxFCCCHEY8JMpVKpDB2EEEIIIURtioqKqtD23//+F4VCwdixY6lfv77WsT59+tC8eXMuXLiAk5MTdnZ2tRVqjVMqlZw9exY7OzucnJwMHc5DUScpp0+fDsCtW7f4+++/SU1NRalUEhQURERERIXblZWVER4ezqZNm7CxsaFHjx60atWKkpIS0tPTOXnyJA4ODkRFRdG5c2et22ZnZzN27FiKiopwcXHB29sbOzs78vPzOXbsGCdPnmTBggUEBgZq3S4rK4sZM2Zw/vx5mjRpgq+vL05OTpSWlnLs2DEyMzMBWLduHe7u7vfte0lJCT179sTLy4vly5c/1ONXXWfPngXQmXh9UJMmTSIzM5O0tDRsbW0f+XpCCCGEMH4yk1IIIYQQT5ywsLAKbevXr0ehUDBu3DiaN2+u83amlJxUs7S0pG3btoYOQy/ufV4PHDjAmDFj+O677wgODq7wvH744Yds2rQJd3d3lixZwjPPPKM5plKpWLNmDQsXLmTSpEkkJibSrl07zfFPP/2UoqIiwsLCNMnRu2VnZ2NhoT3UPnXqFCEhIZSUlDB79mwmTJhQ4Zy8vDw+//xzrl27Vq0+p6SkcPXqVQICAqp1/qPQR3JS7fXXXyctLY0ff/yxQiJXCCGEEE8mWZNSCCGEEKIaKluTUr1O3/Xr1/nkk0/o2bMnzz//PP7+/qSmpgJ3ZitGR0fTt29fPD096dOnD2vWrKn0vnbs2MHEiRPp2rUrHh4e9OnTh88++4yrV69WO16FQkF0dDSDBg3Cy8sLLy8vevfuzYwZMzhy5IjmvMrWpJw7dy6urq7k5OQQHx/Pq6++iqenJ926dSMiIqLSWAoKCliwYIGmr126dGHo0KEsWbJE57nz5s3Dz88PDw8PunbtypQpUzh06FC1+1mVTp060bZtW1QqlVafAfbv309ycjL169dn2bJlWglKADMzM8aMGaNJKi5YsEDreEZGBgBjx47Ved+urq4Vkr/z58/n2rVrTJw4kUmTJlVIUAI0bdqUyMhIvLy8qtXHpKQkbGxs8PPzq3AsKioKV1dX9uzZQ0pKCkOGDKFDhw50796dTz/9VLMm5O+//86YMWPw8vKic+fOvP322xQVFVW4nq41Ke/+u0hPTycoKAgvLy+8vb2ZOHEiJ06c0Bl3nz59sLGxISkpqVr9FEIIIYTpkySlEEIIIcQjUiqVTJgwgbS0NPz8/PD39+fs2bOEhYWxe/duwsLCSEhIoEuXLgQGBlJaWsq8efPYvHlzhWtFR0cTGhrKoUOHePnllwkKCsLZ2ZmVK1cycuRIFArFfeNRqVSEhoYSFRWFnZ0dw4YNY+TIkXTs2JGMjAyysrKq3bfPP/+czz//HFdXV0aPHk3jxo1JTExk6tSpFc49fPgw/v7+xMbG0rhxY8aOHcvgwYOxtbUlOjpa69w//vgDf39/4uLiaN26NUFBQfTq1Yv9+/czatQo0tLSqh1jVW7fvg1QISGYkJAAQGBgYJVl7qGhoVhZWbF7927OnTunaXd0dATgzJkz1Yrj3Llz7N69G2tra0JDQ+97vpWV1X3PUSgUHD58GDc3tyrP/+677wgPD6d169aMHDkSBwcHVq9ezYcffsjmzZuZPHkyDRo0YMSIEbRt25aNGzcyZ86cavVLbfv27YSEhFC3bl1GjBiBj48Pv/32G0FBQRQWFursn5ubG4cOHXqg5LsQQgghTJeUewshhBBCPKILFy7g7u5ObGysJlnk7+/P6NGjCQsLw9nZmZSUFM1alxMmTKB///4sX76cgQMHaq6Tnp5OVFQU3t7eLF++XKu8PDk5mXfffZd///vfhIeHVxlPdnY2WVlZ+Pn5sXTpUq1jt2/frlaiU+3QoUP88MMPNG3aFLiz3uO4cePYt28fBw8epEOHDsCd9R1nzpxJUVERX3zxBYMHD9a6Tn5+vubft27d4p///CelpaV89913+Pj4aI6dP3+eoUOH8t5777Ft2zasra2rHeu99u/fz19//YWlpSWenp5axw4cOADAiy++WOU1GjRogLu7O5mZmWRkZGg2tBk0aBArVqxgypQpjBw5ks6dO+Pu7l7pkgDq+3N3d6+w5unDyszM5Pbt2/ddu3LXrl0kJydrZnaWlZUREBDAhg0bSE1NZeXKlXTp0gX4X4J7x44d/Pnnnzz33HPViiU1NZWYmBh8fX01bV988QXLly8nKSmJSZMmVbiNp6cnGRkZZGRk8PLLL1ez10IIIYQwVTKTUgghhBBCD8LDw7Vms/n4+NC8eXMUCgVz5szRSkw1b94cb29vjh8/Tnl5uaY9NjYWuFMWfG+ya8iQITz33HOkpKTcNxb1TtZ16tSpcMzc3LzS3ap1mTp1qiZBCXdmJA4ZMgS4M3NS7ddffyU3N5fevXtXSFACWuXU27dv5+zZs4wZM0YrQQnQuHFjQkNDuXTpErt37652nHCnvDkqKorIyEhmzZpFcHAwKpWKt956i8aNG2ude+nSJQCaNGly3+uqz7lw4YKmbebMmQwfPpzi4mKio6MZN24cnTt3ZsCAASxatIjc3Fyta1y8eLHa91dd6sRvw4YNqzxv7NixWqXnVlZWDBgwgNu3b9OrVy9NghLuvHZeffVVAI4dO1btWAYNGqSVoAQYNmwYQIVSezV13HcnsIUQQgjx5JKZlEIIIYQQj6h+/fqaGXZ3c3JyIicnBw8PD53HysvLuXTpkiaBlpWVhaWlJT/99JPO+1EqlRQWFnLlyhUcHBwqjaddu3a4ubmRkpJCfn4+vXv3xtvbGw8Pj2qVEd/t3hmI8L+EY3FxsaZNXULeo0eP+15TfW5ubq7OndbVJdSnT59+oBl295aUm5mZ8cknn2iSqrqoE7pVUZ9z97lWVlbMmzePGTNmsGPHDg4ePMjRo0c5cuQIq1atIj4+nq+++oqePXsCd2YoVvf+qku9buT9Zmbqmmmpfs3pOqYufy8oKKh2LLpe47peJ3dr0KABAFeuXKn2/QghhBDCdEmSUgghhBDiEVVW4qteB1HXcfUxpVKpaSsqKuLWrVsVkm33KikpqTJJ+dRTT7F69WqWLFnCzz//zOeffw5AvXr1CAgI4M0338TW1rbqTv1/9erV03l9+N96j4CmhPzeGYu6qJNrW7ZsqfK8kpKSasWolp2drbldZmYm7733Hh9++CHNmzfXmi0Id2bx5eTkUFBQQJs2baq8rjpZ16hRowrHGjZsSEBAgGZ37aKiIhYvXkxiYiLvvvsu27dvx8rK6qESf/ejLoVXb4BTGV2vP/VzWNWxW7duVTuWql7jd79O7nbjxg0AbGxsqn0/QgghhDBdkqQUQgghhHhM1KtXD5VKxd69ex/5Wvb29rz33nu89957/P333+zdu5d169YRGxuLQqHgs88+00PE/6NOUp0/f77a5y5dulTnrtSPytbWlhdffJGvv/6aIUOG8NZbb7Flyxat8vdOnTqRk5PDrl276NatW6XXunr1Kn/88QcA3t7e973vBg0aMG/ePH7//Xfy8vI4ceIE7u7udOrUCbhT+qxQKCpNbD+Ip59+GjDemYjqZLV6EyIhhBBCPNlkTUohhBBCiMdEx44dKS4u5sSJE3q9rrOzM4GBgXz33XfY2tryyy+/6PX6cCd2gJ07d973XPVmO/v379d7HHdr3749gYGBFBQUsHr1aq1jgYGBACQmJmrWp9QlJiaGmzdv0q1bN50l/bqYm5trEqLqMu8WLVrQrVs3bt68yYoVK+57jfvNjgRwdXUF4K+//qpWXI+b06dPA1R7cx4hhBBCmDZJUgohhBBCPCaCg4MBeP/993XOSCwpKdGs51iVc+fO6Ux0FhcXo1QqH2nH7Mr06tWLZs2akZqayubNmyscv7s/fn5+tGzZkri4ONLS0nReLzMzk9LS0keOa+rUqVhbW7Ny5UqttRE7d+6Mv78/RUVFTJ48WWcZ9tq1a/nmm2+wtbWtsKN6dHQ0OTk5Ou9zy5YtnD59Gnt7e1xcXDTtERER1KtXj+XLl7Ny5Uqd5dR5eXnMmjWLzMzM+/bt2WefxdHRsVqvicfRwYMHcXBw0HqMhBBCCPHkknJvIYQQQojHhK+vL7Nnz+bLL7+kX79+9OjRg+bNm1NSUkJeXh779u3D29ubmJiYKq+TnZ3NtGnTcHd3x8XFBScnJwoLC9m6dStKpZKJEyfqPXYrKyu++uorQkJCmDVrFvHx8Tz//PPcuHGD06dPk56eztGjRwGwtLQkKiqK0NBQJk2ahJeXF8899xw2NjYUFBRw+PBhzp07x86dO3XuUP4gGjduzPDhw/n2229ZsWIFs2fP1hybP38+5eXlpKSk0L9/f3r06IGzszOlpaXs2bOH48eP06BBA6KiomjXrp3WdVevXk1UVBRubm54eHjg6OiIQqHg6NGjZGZmYmFhwUcffaS1UVHbtm2JiYkhLCyMzz77jG+//RZfX1+cnJwoKSnh2LFjmuRkdZ4jMzMzXnnlFdatW8eJEyd49tlnH+mxqk2nT58mLy+P4cOH63UzISGEEEIYL0lSCiGEEEI8RiZNmoS3tzexsbEcOHCAbdu2Ua9ePRo3bsywYcMYPHjwfa/h4eHB5MmT2bt3Lzt27KC4uBhHR0fc3d0JCgrS7Ditb56enmzYsIHly5fz22+/kZGRQd26dWnZsiVhYWFa57Zv356NGzeyatUqtm/fTnJyMubm5jRq1Ag3NzfCwsKq3BzoQUyePJnExERiY2MZN24cDRs2BO5sPPPFF18QEBBAQkICWVlZbNu2DWtra5ydnZk+fTpBQUGaXajvtmzZMnbs2KF5jC9duoSFhQWNGzcmMDCQoKAgTTn23Tp27MiWLVtITExk69atbN++natXr2JjY4OzszPjx49n2LBh1S4tHzVqFOvWrWPDhg289dZbj/Q41aYNGzYAMHLkSMMGIoQQQojHhplKvVCOEEIIIYQQwuiEhIRw7Ngxtm7dahQ7ZZeVleHn50fbtm0rrBUqhBBCiCeXrEkphBBCCCGEEXv77be5cuUKcXFxhg6lWuLi4rh06RJz5841dChCCCGEeIxIklIIIYQQQggj5urqyieffFIjGyLVBCsrKxYuXEj79u0NHYoQQgghHiNS7i2EEEIIIYQQQgghhDAomUkphBBCCCGEEEIIIYQwKElSCiGEEEIIIYQQQgghDEqSlEIIIYQQQgghhBBCCIOSJKUQQgghhBBCCCGEEMKgJEkphBBCCCGEEEIIIYQwKElSCiGEEEIIIYQQQgghDEqSlEIIIYQQQgghhBBCCIOSJKUQQgghhBBCCCGEEMKgJEkphBBCCCGEEEIIIYQwqP8Hy8y14PEt/m8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop the bars where n < 4\n",
    "survdelis = []  # survivor indices/minute from ROSC where n < 4\n",
    "nonsurvdelis = []  # Nonsurvivor indices/minute from ROSC where n < 4\n",
    "for i in range(len(ns_dfw)):\n",
    "    if ns_dfw['n - ROSC-CPC 1or2'][i] < 4:\n",
    "        survdelis.append(i - 10)\n",
    "    if ns_dfw['n - ROSC-died'][i] < 4:\n",
    "        nonsurvdelis.append(i - 10)\n",
    "# indices to delete in df2w_sns\n",
    "inds = []\n",
    "for i in range(len(df2w_sns)):\n",
    "    if df2w_sns['time'][i] in survdelis and df2w_sns['ROSC-Survived-CPC-1or2'][i] == 'yes':\n",
    "        inds.append(i)\n",
    "    if df2w_sns['time'][i] in nonsurvdelis and df2w_sns['ROSC-Survived-CPC-1or2'][i] == 'no':\n",
    "        inds.append(i)\n",
    "\n",
    "df2w4_sns = df2w_sns.copy()\n",
    "df2w4_sns.drop(inds, inplace=True)\n",
    "df2w4_sns.reset_index(inplace=True, drop=True)\n",
    "df2w4_sns\n",
    "\n",
    "\n",
    "# Get ns_df2w4 from df2w4_sns\n",
    "ns = []\n",
    "ncpc12 = []\n",
    "nnot = []\n",
    "for min in range(df2w4_sns['time'][0], df2w4_sns['time'][len(df2w4_sns) - 1] + 1):\n",
    "    n = 0\n",
    "    cpc12 = 0\n",
    "    cpcnot = 0\n",
    "    for index, row in df2w4_sns.iterrows():\n",
    "        if df2w4_sns['time'][index] == min:\n",
    "            n += 1\n",
    "            if df2w4_sns['ROSC-Survived-CPC-1or2'][index] == 'yes':\n",
    "                cpc12 += 1\n",
    "            else:\n",
    "                cpcnot += 1\n",
    "        elif df2w4_sns['time'][index] > min:\n",
    "            break\n",
    "    ns.append(n)\n",
    "    ncpc12.append(cpc12)\n",
    "    nnot.append(cpcnot)\n",
    "ns_df2w4 = pd.DataFrame(list(range(df2w4_sns['time'][0], df2w4_sns['time'][len(df2w4_sns) - 1] + 1)), columns=['Time since Call Pickup (min)'])\n",
    "ns_df2w4['n'] = ns\n",
    "ns_df2w4['n - survived CPC 1 or 2'] = ncpc12\n",
    "ns_df2w4['n - not'] = nnot\n",
    "ns_df2w4\n",
    "\n",
    "\n",
    "\n",
    "#Plot the boxplot\n",
    "sns.boxplot()\n",
    "sns.boxplot(data=df2w4_sns, x=\"Time since ROSC (min)\", y=\"rSO2 (%)\", hue=\"ROSC-Survived-CPC-1or2\", palette=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d93774-4dfb-470f-b40a-bd58aa7757dc",
   "metadata": {},
   "source": [
    "## Linear Mixed Effects Model - Pre-ROSC\n",
    "From here: https://www.statsmodels.org/dev/mixed_linear.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de724759-79e6-4d37-8b90-75148930619a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75201a9-9feb-4dda-a3a6-1689de42cfb6",
   "metadata": {},
   "source": [
    "### Mixed Linear Model - Survived with CPC 1 or 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7927f54e-46a4-4919-bc03-2a4aecc1e1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mixed Linear Model Regression Results\n",
      "=======================================================\n",
      "Model:            MixedLM Dependent Variable: b_ox     \n",
      "No. Observations: 91      Method:             REML     \n",
      "No. Groups:       8       Scale:              93.1992  \n",
      "Min. group size:  9       Log-Likelihood:     -344.6064\n",
      "Max. group size:  14      Converged:          Yes      \n",
      "Mean group size:  11.4                                 \n",
      "-------------------------------------------------------\n",
      "             Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "-------------------------------------------------------\n",
      "Intercept    54.534    4.767 11.440 0.000 45.191 63.877\n",
      "time          0.997    0.265  3.766 0.000  0.478  1.517\n",
      "Group Var   166.220   10.158                           \n",
      "=======================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_survived = df2_sns[df2_sns['ROSC-Survived-CPC-1or2'] == 'yes'].copy()\n",
    "md_survived = smf.mixedlm(\"b_ox ~ time\", df2_survived, groups=df2_survived[\"patient\"])\n",
    "md_survived_f = md_survived.fit()\n",
    "print(md_survived_f.summary())\n",
    "\n",
    "surv_intercept = round(md_survived_f.params['Intercept'], 2)\n",
    "surv_time_coef = round(md_survived_f.params['time'], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241f63a9-8688-459d-a501-2775aae13a2a",
   "metadata": {},
   "source": [
    "### Mixed Linear Model - Died"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bdc12a7e-5495-4147-8706-258be55dc06e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mixed Linear Model Regression Results\n",
      "========================================================\n",
      "Model:            MixedLM Dependent Variable: b_ox      \n",
      "No. Observations: 305     Method:             REML      \n",
      "No. Groups:       22      Scale:              70.9225   \n",
      "Min. group size:  5       Log-Likelihood:     -1120.2993\n",
      "Max. group size:  21      Converged:          Yes       \n",
      "Mean group size:  13.9                                  \n",
      "--------------------------------------------------------\n",
      "              Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "--------------------------------------------------------\n",
      "Intercept     46.358    2.987 15.519 0.000 40.503 52.212\n",
      "time           1.378    0.102 13.525 0.000  1.178  1.578\n",
      "Group Var    190.025    7.432                           \n",
      "========================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_died = df2_sns[df2_sns['ROSC-Survived-CPC-1or2'] == 'no'].copy()\n",
    "md_died = smf.mixedlm(\"b_ox ~ time\", df2_died, groups=df2_died[\"patient\"])\n",
    "md_died_f = md_died.fit()\n",
    "print(md_died_f.summary())\n",
    "\n",
    "died_intercept = round(md_died_f.params['Intercept'], 2)\n",
    "died_time_coef = round(md_died_f.params['time'], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112a0a5d-1bec-4002-ac5f-fc710fbc69ac",
   "metadata": {},
   "source": [
    "### Plot linear mixed model fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7161d336-78ed-48d5-8fbe-70609d7d3a6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import numpy as np\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the slope and intercept for the two functions\n",
    "m1, B1 = surv_time_coef, surv_intercept  # Function 1: y = m1 * t + B1\n",
    "m2, B2 = died_time_coef, died_intercept  # Function 2: y = m2 * t + B2\n",
    "\n",
    "# Generate values for t\n",
    "t = np.linspace(-10, 10, 100)\n",
    "\n",
    "# Calculate the corresponding y values for both functions\n",
    "y1 = m1 * t + B1\n",
    "y2 = m2 * t + B2\n",
    "\n",
    "# Set up the plot\n",
    "sns.set_theme(rc={'figure.figsize':(6,4)})\n",
    "sns.lineplot(x=t, y=y1, label=f'Survived: y = {m1}*t + {B1}', color='blue')\n",
    "sns.lineplot(x=t, y=y2, label=f'Died: y = {m2}*t + {B2}', color='orange')\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Mixed Effects Regression from [-10, 10] min')\n",
    "plt.xlabel('Time since ROSC (min)')\n",
    "plt.ylabel('rSO2 (%)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d200a3-def3-4e30-8bb0-cdab7f71a598",
   "metadata": {},
   "source": [
    "## Linear Mixed Effects Model - Post-ROSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c9e9a5c3-7ece-4356-8c0d-8a8fc34712e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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/UpfQs1JalhpXV1cqV66c7vVf5OHDhwAMGzYs1XXCwsJQq9UYDIZMNb9DYtwrV65k3rx5TJs2jbCwMLy8vHjrrbd4//33zQqBlDg4OLBq1SpTzEuWLGHbtm34+PjQv39/s1xSK9zU6sTvLE+ePAEgX758ydbx9PRM8SLu5eVl9nPSsSZNmsSkSZNSPF7SccaOHUvBggXZvn07CxcuxN7ensDAQEaOHEnJkiVRqVQsW7aM2bNns2jRIr799lvy5MlDu3bt+OCDD8wKjGeFhobi6emZ7AMuX758aLVas24mSCwCnqfT6YiLi0tx/+n1/O8m6bj58+dPtm7+/PmTfcilFpe7u3uyZUCm43V3d0elUiX7vUDi33jSOq9KUp5JfxfPCg0NRaVSmd25lBnPx5/0vkpp+fPjmlLz/PlJOg/PF90ia0ixkkt17NiRmTNn8tNPP5l9C08vg8HAxx9/jLu7Ozqdjk8//ZQ1a9aY3rB58+ZFpVLx448/pnh3SNKypDd80ofcsx4+fGixW0Hz5s0LwGeffYafn1+K6+TLlw+j0YhGo3mpgXU+Pj5MmzYNRVG4ePEimzZtYvbs2Tg4OJgKjtSo1WqzIq1evXp07NiRWbNm0a5dOwoVKmTKZebMmRQuXDjVfSWt9/zAXEj5/EDyb7xJ+xgwYADNmzdPcZuksQ9OTk4MGzaMYcOG8fDhQ/bv38/333/PwIED2blzJ5A4yHLixIlAYsvPjh07mDVrFvHx8UyYMCHF/efJk4dTp06hKIpZfI8ePUKv15tifNWe/90k/a0/ePAg2a2wDx48yLa4nufg4ECJEiW4dOlSstcuXryIg4PDK72rrHjx4jg4OKR4/EuXLlGiRImXGq8ibIN0A+VSBQoUoE+fPjRu3Jj27dtnePuZM2fyzz//MGXKFKZNm8bFixeZPHmy6fVGjRqhKAr379+ncuXKyf4lNZlWrVoVe3t7tm7darb/48ePv3Qz/MuoVq0abm5uXLlyJcX4K1eujE6nw8HBgZo1a7Jz585kk2w9Kz3fulQqFeXLl2fMmDG4ubllakIunU7HuHHjiIuLY+7cuQAEBgai1Wq5ceNGqrlAYhHh5eXFjh07zPZ5584dTpw4ka7jly5dmpIlS3LhwoVUj5VSi4inpycdO3akTZs2XLt2jZiYmGTrlCpVikGDBuHt7Z3iXUxJ6tSpQ3R0dLK7SLZs2WJ63RICAgIA+OWXX8yW//vvvwQHB5tet4RmzZpx+PBh7t69a1oWGRnJ77//TpMmTdBqX933Wq1WS+PGjfn999/Npk64c+cOR44cSbXoFbmLtKzkYh9//HGmtjt06BALFixg0KBBpgv/Rx99xKRJk6hduzbNmzenevXqdOnShTFjxnDmzBlq1qyJo6MjISEhHDt2DG9vb9MkVL1792bu3Ll8+umntGrVinv37vHDDz8ka0ZPS3h4OCdPnky2XKfTUbFixQzn6OzszNixYxk1ahRhYWG0bNmSfPny8fjxYy5cuMDjx48ZP348AKNHj+btt9/mrbfeon///hQvXpxHjx6xd+9exo8fj4uLC+XKlQMSbwt2dnbG3t6eokWLcvLkSVavXk2zZs0oVqwYiqKwa9cuwsPDqVevXobjhsQxSQ0bNmTTpk3069ePYsWKMWzYMKZPn87Nmzdp0KABbm5uPHz4kNOnT+Po6MiwYcNQq9UMHTqUcePGMWzYMN58803Cw8NNk4ild9zA+PHj6devH3369KFDhw4UKFCAsLAwgoODOXv2LDNnzgQSbxVv1KgRPj4+uLu7ExwczM8//4y/vz+Ojo5cuHCBL7/8klatWlGiRAns7Ow4fPgwFy9eTLPFqX379vz444988skn3L59G29vb44dO8b8+fNp2LBhql2Tr1rp0qXp0qULq1atQq1W06BBA9PdQIUKFeK9996zSFwAffr04eeff6Z///4MHz4cOzs7Fi5cSFxcXLLJ10aNGsXmzZvZs2fPCyc4DAoKIiYmxnRn25UrV0ytZg0bNsTR0RGAoUOH0qlTJwYOHEi/fv2Ij49n5syZ5M2bN8U7rUTuI8WKyJAHDx4wYsQIatWqxeDBg03Le/XqxdGjRxkzZoxp0qkJEybg5+fH2rVrWbNmDUajkfz581OtWjWzydeGDx+Ok5MTq1ev5ueff6Z06dKMHz8+QzOwHj9+nC5duiRbXqBAgUzPQfHGG29QuHBhFi1axOeff05UVJRpgOOzgyTLly/Phg0bmDlzJt9//z1RUVF4eXkREBBgalEpVqwYY8aMYcWKFfTs2RODwcA333xD1apVcXNzY9GiRTx48AA7OztKlSrFt99+m2wgZkZ8/PHHvPHGG8yZM4dvvvmGAQMGUKZMGVasWMGvv/5KfHy8aQDn22+/bdquS5cuqFQqFi1axODBgylSpAj9+/dnz549Zt+60xIQEMD69euZN28eEydOJDw8nDx58lCmTBmzwbsBAQHs3buX5cuXExMTQ4ECBWjfvj0DBw4EEsd8FC9enNWrV5vmISlWrBiffPJJmtPr29vbs2LFCqZNm2aa7KxAgQL07t0722Y9Tc0XX3xBsWLF2LBhA6tXr8bFxYX69evzv//9z2LdQAAeHh6sXr2aSZMm8cknn2AwGKhatSorV640DUZNEh0djYODQ7rGkYwfP96shXTnzp2mYuXZYqdMmTKsXLmSKVOmMHz4cDQaDQEBAcyePTtTA9eF7VEpiqJYOgghhPUKDw+nZcuWNGvWjC+//NLS4YhM2rRpE6NHj+b333+ncOHCme7aqVevHq+//nqKj7fIDnq9njt37tC8eXNGjhyZbHC/sE3SsiKEMAkJCWHevHnUrl2bPHnycOfOHZYtW0ZUVBQ9e/a0dHgiCySNAfnrr78y3Gpx+fJlYmJi6Nev36sI7YXCw8OpWbOmRY4tLEtaVoQQJmFhYXzyySecPn2asLAwHBwc8PPzY9iwYaneFSVyhidPnpg9U6dChQqvdODsq2AwGMwGVxcqVChDUxyInEuKFSGEEEJYNbl1WQghhBBWTYoVIYQQQlg1KVaEEEIIYdWkWBFCCCGEVctZQ8GzmKIoGI22P75YrVblijwh9+QqedoWydO25IY81WpVtjyNO0muLlZUKhXh4dHo9S9+7HxOpdWqyZvX2ebzhNyTq+RpWyRP25Jb8vTwcEajyb5iRbqBhBBCCGHVpFgRQgghhFWTYkUIIYQQVk2KFSGEEEJYNSlWhBBCCGHVpFgRQgghhFWTYkUIIYQQVk2KFSGEEEJYNSlWhBBCCGHVpFgRQgghhFWTYkUIIYQQVi1XPxtICGGdIiPho4/scXODunU11KunUKCAbT8YTgiROilWhBBW59YtNT//rEFRYMUKBwAqVjTQsKGBhg31BAQYcHKycJBCiGwjxYoQwuqUL2/kt99i2bPHkZ07DZw6peHcucR/c+fqsLdXqFUrsXhp1EiPr68RtXRqC2GzVIqi5Oq21SdPomz6Md5Jjyu39Twh9+SaG/O8d0/hwAEN+/ZpCQrScOeOeWWSL5+RBg0SW10aNjRQpEjOuazlxvMpeeZ8Hh7OaDTZ9w1BWlaEEFbP01OhQwc9HTroURS4ckVNUJCGoCAtBw9qePRIzebNajZvtgOgbNmnrS716hlwcbFwAkKIlyLFihAiR1GpoFw5I+XKGenbN4GEBDh2TMO+fYnFy4kTaq5c0XDliobFi3VotQrVqz8d7+Lvb0QrVz4hchTpBrLxprrc0iQJuSdXyTNtYWFw8KDWVLxcv27eVO3mphAYqDe1vJQqZdlLoJxP25Jb8pRuICGEeAnu7tCmjZ42bfRAHNevqwgKShzrcvCgltBQFdu327F9e2KXUfHiRho21NOokYHAQD1581o2fiFEclZTrKxfv56VK1dy7do1XFxc8PPzY968eRgMBpYsWUJQUBBXrlzBYDDg7e3NkCFDqFOnjqXDFkJYuZIlFUqWTODddxMwGODUKbWpePn7bw03bqhZuVLHypWgVitUrWo0DdStUcOATmfpDIQQVlGs/PDDDyxbtoyBAwfi5+dHWFgYBw4cACA2Npb58+fTvn17+vTpg1arZfPmzfTq1Yu5c+fSuHFjC0cvhMgpNBqoVs1ItWrxfPhh4uRzf/2V2F20b5+GS5c0HD+e+G/aNHByUqhX7+ldRt7eRlQqS2chRO5j8TErwcHBtGvXjgULFhAYGJjsdYPBQGRkJO7u7qZliqLw5ptv4uzszMqVK1/q+Lber5hb+k8h9+Qqeb46d+6o2L8/8Rbp/fs1PHxo3idfqFDiLdKNGulp0MCAl9fLXz7lfNqW3JJnrhuzsmnTJooVK5ZioQKg0WjMChUAlUpF+fLlOXbsWHaEKITIJQoXVujaVU/XrnqMRjh79ukt0keOaLh7V83atWrWrk0c71Kp0tNbpGvXNuDoaOEEhLBRFi9WTp06hbe3N7Nnz2bVqlVERERQtWpVPv30UypUqJDiNkajkRMnTlCmTJlsjlYIkVuo1VC5spHKlY0MGZJATAwcPfp0YrozZzScPZv4b84cHQ4OCrVrP+0yqlRJZtUVIqtYvFgJCQnh7NmzXL58mfHjx2NnZ8esWbPo1asXu3btws3NLdk2SQNxJ0yY8NLHz85mLEtIys/W84Tck6vkaRmurtC0qULTpglAAg8ewP79Gv74I3GOl7t3kwbuJl5WPT2V/291SfyX2qy61pbnqyJ52pbsHrtl8TErLVq04L///mPbtm2UK1cOgAcPHtC0aVOGDRtGv379zNY/evQovXv3pmfPnowcOdISIQshhBlFgQsX4PffE//98QdERZmvU6ECNG+e+K9RI2RWXSEywOLFSufOnblz5w6HDh0yW/7GG29Qvnx5Jk2aZFp24cIFunfvTmBgINOmTUOVBaVdeHgMBoPtDoLSaNS4uTnafJ6Qe3KVPK1ffDz884+aP/5IbHk5eVKN0fj0emVnp1CzppFGjQw0a2akQQMHoqJyXp4ZkZPPZ0bkljzd3R1RZ2M/p8W7gcqUKcOdO3eSLVcUxewXcePGDfr27UvFihWZPHlylhQqAAaD0aZHbCfJLXlC7slV8rReajXUqmWkVi09n3wCoaFw4EDiWJd9+7TcuKHmzz81/PmnhokTIW9eCAy0o0EDPY0a6SlRwnYnFs+J5zMzbD3P7G7msHix0qhRIzZv3sylS5fw9vYG4P79+1y9epWOHTsCieNaevfujaenJ3PmzEEnszQJIXKQPHmgXTs97dolzqp77Zr5rLpPnqjYulXL1q2Jl+SSJZ9OTFe/vp7nbogUItexeDeQwWCgc+fOREVFMXz4cHQ6HbNnz+bRo0fs3LkTtVpNly5duHHjBt999x2enp5m21etWvWljm/r98Lnlnv+IffkKnnaGjXBwc788ks8e/eqOXZMg17/tOVYrVbw93/6SIDq1Q3Y2Vkw3EzKLeczt+SZ3fOsWLxYAXj06BETJ04kKCgIvV5PzZo1GT16NKVLl+bWrVs0bdo01W0vXrz4Use29T+o3PLGgdyTq+RpW57PMzISDh16OqvulSsas/WdnRUCA5/eIl22bM6YVTe3nk9blSuLFUuy9T+o3PLGgdyTq+RpW16U561bibPqBgUlzqr76JH5B0ThwkYaNUrqMjLg6Wmdl3Q5n7Yl181gK4QQInVFiyp066anW7ens+ru25fY6nL0qIY7d9SsXq1j9erE9X19E2fUbdTIQK1aBhwcLBu/EFlBihUhhMghns6qG8/QoRAdDYcPa0yDdc+dS5xZ98wZDbNmgaNj8ll1c0KXkRDPk2JFCCFyKCcnaNLEQJMmBgAePHjaZbRvn4b795NaYRIv9V5eiQ9iTBqsW7CgdXYZCfE8KVaEEMJG5M+v0KmTnk6d9CgKXLz49EGMf/6pISREzcaNajZuTLydyMcn8VEADRvqqVPHgLOzhRMQIhVSrAghhA1SqaB8eSPlyxsZMCCBuDj45x+NaWK6U6fUXLyo4eJFDfPn6/5/Vt2nT5GuUsWIRvPi4wiRHeRuIBsfsZ1bRqZD7slV8rQtlsrz8WM4ePDprLo3b5rf2ZE3r0L9+oljXRo21FO8+Mt9VMj5tC1yN5AQQohXzsMDXn9dz+uv61GUxFl1k+4yOnQocVbdX36x45dfEruMSpc2/v/jAAwEBupxc7NwAiJXkWJFCCFyOZUKSpdWKF06gd69E9Dr4fhxtekuo2PHNFy9qubqVR3LloFGkzirbtL8LtWq5cxZdUXOId1ANt5Ul1uaJCH35Cp52packGdEROKsuvv2aQkK0hIcbN787+KiEBioN413KV1aSXaLdE7IMyvkljylG0gIIYRVcXWFVq0MtGplAOK4eVPF/v2JrS7792t4/FjNzp127NyZ2LxStOizD2I0kC9frv5OLLKAtKzYePWbW6p8yD25Sp62JafnaTTC6dPq/291SZxVNz7+abOKSqVQpYqRxo0NtGuno2LFKDSanJdneuX085le8mygbGbrf1C55Y0DuSdXydO22FqeUVFw5IjGVLycP29+/7Ojo0JAgME03qVCBduaVdfWzmdqpBtICCFEjuXsbD6r7v37KoKCNBw4oCUoyI5791T88YeWP/5I/PgpUODprLoNGxooUCBXf38WqZBiRQghxCtToIDCW2/p6dbNSJ48dvz5ZzR79iR2Gx0+nPhIgPXr1axfnzjepUKFpxPTBQQYcHKycALCKkixIoQQIluoVFCxooK3dwLvv59AbCz8/bfG9EiAf/9Vc/58YtfRvHk6dDqFWrWeFi+VKxtRZ1/Pg7AiMmbFxvsVc0v/KeSeXCVP2yJ5PvXokYoDB54WL7dumVcmHh5JXUYGGjTQU6yY9X185ZbzKWNWhBBC5Er58im0b6+nffvEWXWDg1WmiekOHtTy+LGaLVvUbNmS2GVUpszTW6QDA/W4ulo4AfHKSLEihBDC6qhUULasQtmyCfTpk0BCAhw//rTV5fhxNcHBaoKDdSxZkjirbrVqSbPq6qlWzYhWPuFshnQD2XhTXW5pkoTck6vkaVskz8wJDzd/EOO1a+ZdEq6u5rPqliqVfFbdVyG3nE/pBhJCCCFewM0NXntNz2uv6YE4btx42mW0f7+W0FAVO3bYsWNHYpdR8eLPzqqrJ29ey8YvMkaKFSGEEDle8eIKPXok0KNHAgYD/Ptv4oMY9+3T8PffGm7cULNypY6VKxNn1a1a9WnxUrOmAZ3O0hmItEixIoQQwqZoNODvb8TfP54PPoDISDh8WGMqXi5e1HDiROK/6dPByUmhTp2ns+r6+NjWrLq2QIoVIYQQNs3FBZo1M9CsWeKsunfvqkwDdYOCNDx8qGbPHi179iR+JBYsmHiLdKNGeho0MJA/f64e2mkVpFgRQgiRqxQqpNC1q56uXfUYjXDunNpUvBw+rOHePTXr1qlZty5xvEvFiolzuzRsKLPqWorVFCvr169n5cqVXLt2DRcXF/z8/Jg3b57p9aCgIKZNm0ZwcDAFCxbkvffe45133rFgxEIIIXI6tRp8fY34+hoZPDhxVt2jRzXs25dYvJw+reHcucR/c+fqsLd/Oqtu48Z6KlWSWXWzg1UUKz/88APLli1j4MCB+Pn5ERYWxoEDB0yvnzhxgkGDBvHGG28watQojh8/zldffYVOp6Nz584WjFwIIYQtcXCABg0MNGhgAOJ5+PDprLr79mm5c0fNgQNaDhzQ8tVX9nh6Gqlf/+mDGEuUsHQGtsni86wEBwfTrl07FixYQGBgYIrr9O3bl7CwMNavX29a9tlnn/HHH3+wf/9+1C9R1tr6vfC55Z5/yD25Sp62RfLMORQFrlx52mV08KCGqCjzkbjlyhlp1UpNnTqxBAQk4OJioWBfsVw3z8qmTZsoVqxYqoVKfHw8hw8f5uOPPzZb3q5dO9atW8e5c+fw9fXNjlCFEELkYipVYjFSrpyRvn0TZ9U9duxpl9GJE2ouX1Zz+TL88IMDWq091asbaNQoseWlalWZVTezLN7TdurUKby9vZk9ezZ16tTB19eX7t27c/78eQBu3LhBQkICpUuXNtuubNmyQGLLjBBCCJHd7OwgIMDAqFHx7NgRzYULkSxfHsvAgVCqlBG9XsWRI1omTbLntdecqVDBhffec2DpUjuuXZN7ozPC4jVeSEgIZ8+e5fLly4wfPx47OztmzZpFr1692LVrF2FhYQC4ubmZbZf0c9LrmZWdzViWkJSfrecJuSdXydO2SJ62w9MT2rdX6NkTwsPjCA5W2LdPwx9/aNi/X0NYmIrt2+3Yvj3xLqMSJYw0bpzY8tKggYE8eSwbf0Zk9zw0Fi9WFEUhOjqaH374gXLlygFQqVIlmjZtytq1a6lWrRoAqlR+M6ktTy83N8eX2j6nyC15Qu7JVfK0LZKnbXFzc8TfH/z94cMPwWCAY8fg999h1y7480/47z81y5apWbbMDrUaatSA5s2hRQsICEBm1X2GxYsVd3d3PD09TYUKQP78+SldujRXrlyhcePGQPIWlPDwcCB5i0tGhYfHYDDkzMFe6aHRqHFzc7T5PCH35Cp52hbJ07aklWe5con/Bg1KnFX30KHEVpd9+zRcuqTm6FE4ehS+/hqcnRXq1UtsdWnUyICPT/Y8iDG93N0dX+rmloyyeLFSpkwZ7ty5k2y5oiio1WqKFy+OnZ0dV69epUGDBqbXr1y5Ytr+ZRgMxhw7Mj0jckuekHtylTxti+RpW16Up4MDNG1qpGnTBODprLr79mnZvz9xVt1du7Ts2pX4MV2okNE0MV2DBga8vCw7q25230ds8c7DRo0a8fDhQy5dumRadv/+fa5evYqPjw86nY6AgAB27Nhhtt22bdvw8vKiYsWK2R2yEEIIkaWSZtWdNy+WM2ei2LMninHjYmnYUI+9vcLdu2p++smO9993pFIlF5o0cWL8eHv27dMQG2vp6F89i8+zYjAY6Ny5M1FRUQwfPhydTsfs2bN59OgRO3fuxMnJiRMnTtC9e3c6dOhAu3btOH78ODNnzmTChAkvPSlcTr7nPz1sYW6D9MotuUqetkXytC2vIs+YGDhyJLHVJShIw9mzGrPXHRwUatd+OjFddsyqm93zrFi8WAF49OgREydOJCgoCL1eT82aNRk9erTZ7cpBQUFMnTrVNN1+r169smS6fXnj2I7ckqvkaVskT9uSHXmGhCTOqptUvNy9a140eHo+fRBjw4YGChXK+o/5XFmsWJK8cWxHbslV8rQtkqdtye48FQUuXXo6q+6hQxqio81H4np7Jz7LqFEjPXXqGLJkVt1cN4OtEEIIITJHpQIfHyM+Pkb6908gPh7++UdjKl5OnlRz6ZKGS5c0LFyow85OoWbNp0+R9vMzotG8+DiWJi0rUuXbjNySq+RpWyRP22JteT55AgcPak2PBLhxw7w1JE8ehcBAvanlpUSJ9JUE0rIihBBCiCyRNy+0a6enXTs9EMe1ayqCghKLl4MHtYSGqti2zY5t2xJn1S1Z0mgaqFu/vh53d8vGn0SKFSGEECKXKFVKoVSpBN57LwG9Hk6eVBMUlDhQ959/NFy/rub6dR3Ll4NareDvn1i8NGpkoHp1A3Z2lolbuoGspKnuVbG2JslXKbfkKnnaFsnTtuTkPJNm1U0qXi5fNh/M4uKSOKtuw4Z6PvxQh4ODdAMJIYQQIhu5uEDLlgZatjQAcOuWiv37E4uX/fs1PHqk5rfftPz2m5YuXeCZ2UVeOSlWhBBCCJFM0aIK3brp6dZNj9EIZ8+q+eOPxFYXlUoDZN/DiqRYEUIIIUSa1GqoXNlI5crxDBsGefM6k53FisWfDSSEEEKInCW7nwAtxYoQQgghMkSlJGTr8aRYEUIIIUS62T3aiyr2TrYeU4oVIYQQQqSL/b2NuJ/ojIrsnfVEihUhhBBCvJDDzQW4nu6NSklA0Thn67HlbiAhhBBCpE5RcLo6EeerkwCIKdYPnc6L7Hz+oRQrQgghhEiZYsDlwsc43loMQFTpMUSX/gSPbA5DihUhhBBCJGeMw/VMfxzub0ZBRWT574kt1tcioUixIoQQQggzKn0EbqfeQfd4H4rKjgjfhcQV7GixeKRYEUIIIYSJKv4h7sffxC7iBIrGmTC/1STka2zRmKRYEUIIIQQA6pgbuB9vjzb6Cka7fIT5b0DvXt3SYUmxIoQQQgjQRJ7H/Xh7NHF3MTgUI6zaFgzO5SwdFiDFihBCCJHraUOP4H6iM2p9KHrnCoRV24zRobClwzKRYkUIIYTIxXQPd+F2qgcqYwwJ7rUI81+HYpfdNyenTWawFUIIIXIp+7s/4XayKypjDHH5mhNa/WerK1RAihUhhBAiV3L8bzZuZ/qjUvTEFupCeNWfIJun0U8vixcrmzZtwsfHJ9m/KVOmmNbR6/UsWLCAVq1a4efnR5MmTfjqq68IDw+3YORCCCFEDqQoOF/+ApdLowGILj6IiErzQW1n4cBSZzVjVhYtWoSrq6vp5wIFCpj+P3v2bBYsWMDQoUOpWrUqwcHBTJs2jVu3bjFv3jxLhCuEEELkPEY9Luc/wPHOCgAiy35BTMkPQaWycGBps5pipVKlSnh4pNxPtm3bNtq2bcvAgQMBCAgIIDo6mqlTpxIdHY2Tk1N2hiqEEELkPIZY3E73xj5kGwpqIivMILbou5aOKl2splhJi16vN2t1AXBzc0NRFBRFsVBUQgghRM6gSgjD7dTb6J4cRFHbE155CfH521k6rHSz+JiVJG3btqVChQo0bdqU+fPnYzAYTK916dKFn3/+mT///JOoqChOnz7NkiVL6NChA87O1jkYSAghhLAGqrj75PnnNXRPDmLUuhHmvzlHFSpgBS0rXl5eDB06FD8/P1QqFXv37mX69Oncv3+fcePGATBw4ED0ej29e/c2taS0aNGCCRMmvPTxNRqrqddeiaT8bD1PyD25Sp62RfK0LdaWpzrqKi5/v4Em5hpGnReRNbaguPu99Id/dg9xUSlW2I8yadIkli9fzr59+8ifPz+rVq1i+vTpDBkyhEqVKnHt2jVmzJhBYGAgkyZNsnS4QgghhPV5cgr+aAmx98G5FDTZBa5lLR1VplhlsfLvv//SuXNnFixYQJUqVWjQoAEjRoygZ8+epnV2797N4MGD2bRpE5UqVcr0scLDYzAYjFkRtlXSaNS4uTnafJ6Qe3KVPG2L5GlbrCVP7aMDuBzvgkofjt7VN7FFxaFglu3f3d0RtTr7Wo8s3g30Ijdv3iQ+Pp4KFSqYLU/6+caNGy9VrBgMRvR6233jJMkteULuyVXytC2Sp22xZJ66B7/icvo9VMY44vPUJbzqTyjaPJCF8WR3M4d1dKo9Z/v27Wg0GipWrEjhwokPUjp79qzZOmfOnAGgSJEi2R6fEEIIYY0cbq/E7dQ7qIxxxHm9Rli1zSh2eSwd1kuzeMtKnz59CAgIwNvbG4A9e/awbt06evbsiZeXFwAtW7ZkxowZ6PV6fH19uXr1Kj/88AP+/v74+vpaMnwhhBDC8hQFx/9m4HI58caUmMLdiawwE9QW/5jPEhbPolSpUmzYsIF79+5hNBopWbIkY8aMoUePHqZ1Jk6cyNy5c1m3bh0zZ87E09OTFi1aMHz48GztMxNCCCGsjmLE+fJnOP33AwDRJT8gqux4q5+VNiOscoBtdnryJMqm+0+1WjV58zrbfJ6Qe3KVPG2L5Glbsj1PYwKu54bgcHcNAJHlviam5NBXflgPD+dsvT3b4i0rQgghhMgEQzRu/76L/cPfUFQaIirOIq7wO5aO6pWQYkUIIYTIYVQJT3A/8RZ2YUdQ1A6EV1lOvFdrS4f1ykixIoQQQuQg6ti7uJ/ogDbyHEZtHsKqrkWft46lw3qlpFgRQgghcghN1GXcj3dAE3sDg64gYdU2Y3DN/FxjOYUUK0IIIUQOoA0/gfvxN1EnPETvWJqw6lswOpa0dFjZQooVIYQQwsrZPQ7C7eTbqA2RJLhWJazaRhSdl6XDyjYySYkQQghhxXT3tyS2qBgiic/bgLAa23JVoQJSrAghhBBWy+HWEtz+fReVEk9c/jcI89+AonWzdFjZTooVIYQQwtooCk5XJ+F6/gNUKMQU6U14lWWgcbB0ZBYhY1aEEEIIa6IYcb74CU435wMQVWok0WU+tanp8zNKihUhhBDCWhjjcT07EId7GwCI9JlETPH3LRyU5WWqWLl8+TLHjx/n/v37xMbGkjdvXsqWLUvNmjVxcXHJ6hiFEEII26ePxP3fHuge7UFRaYmoNI+4Qm9ZOiqrkO5iJSwsjLVr17J27Vru3LlDSs8/1Gq1NGjQgB49elCnjm3PpieEEEJkFVX8I9xPdsYu7B8UtRNhfqtI8Gxm6bCsRrqKlRUrVjB79mwAXnvtNWrVqkWlSpXw8PDA3t6esLAwbt68ycmTJ9mzZw+9e/embt26jBs3jhIlSrzSBIQQQoicTB17C/fjHdBGXcRol5cw/w3o3WtaOiyrkq5iZeXKlYwePZo2bdpgZ2eX7HVPT088PT3x9/enV69e3Lhxg7lz57Jjxw4GDhyY5UELIYQQtkATeRH34+3RxN3GYF+EsGpbMLj4WDosq5OuYmXHjh1otekf3lK8eHG++eYbDAZDpgMTQgghbJk27G/cT3RCnfAEvbM3Yf6bMToWs3RYVildFUhGCpVnaTSaTG0nhBBC2DK7h7txP9UdlTGaBLfqiZO96fJZOiyr9dK3Lh89epSgoCAURaFBgwYEBARkRVxCCCGETbK/twHXMwNQKQnEezQmzO9H0MqdtGl5qRlsf/rpJwYMGMCNGze4ePEiffr0YdWqVVkVmxBCCGFTHG7Mw/V0H1RKArEFOhLmv14KlXR4qZaVJUuWsGLFCipXrgzA8uXLWbJkCd27d8+S4IQQQgiboCg4BX+N87XJAMQU60ekz3egkqfepEe6ipUePXrw5ZdfUrJkSbPlERERlC5d2vRzyZIliYyMzNIArYHRaMRg0Fs6jEwxGlXExmqIj4/DYEg+N44tyS25Sp62Ra1OfoelsDGKAZfz/8Px9hIAosp8SnSpkbl6+vyMSlexUrZsWdq3b0+/fv3o37+/6fblunXr8uGHH9KtWzeio6OZM2cOdevWfaUBZydFUQgPf0xMTM4uwB4+VGM0Gi0dRrbILblKnrYlLs4DB4fc9yTdXMEYh9vpftg/2IKCisjyU4kt1sfSUeU4KiWlqWhTcPLkScaNG4der+fLL7+kevXqhIWF8dVXX3HgwAEURSEwMJBPP/0UDw+PVx13lnnyJAq9PuWLYVjYI2JiInFxyYtOZ48qh1bBGo3Kpr+ZPiu35Cp52gZFUYiPjyM6OgxHR2dcXXPOtTOjtFo1efM6p3nNtQXP5mmIDcPtVDd0j4NQVDrCKy8ivkB7S4eYJTw8nNFosq8LK93FCoBer2fx4sXMmzePdu3aMXLkyBz/LKDU3jhGo4EHD27h4pIXF5ec/Y1Hq1Xb9MXhWbklV8nTtsTERBAW9pj8+YuhVtvmGIbcVqyE3ruG899vYhdxAqPGhXC/1STka2Tp8LJMdhcrGTqSVqtlwIAB/Pzzz9y8eZNWrVqxffv2lwpg06ZN+Pj4JPs3ZcoUs/ViY2OZOnUqjRs3xtfXlyZNmjBr1qyXOnZakia00+nsX9kxhBACwN4+8TqTU8fGiedEXsf1cIvEQsXOk7Aav9pUoWIJ6b4bSFEUrl+/TlxcHKVLl2bp0qVs2bKFr776ii1btvDFF19QuHDhTAeyaNEiXF1dTT8XKFDA9H+DwcCAAQO4d+8ew4YNo0iRIty5c4e7d+9m+njplVO7foQQOYlcZ2yFOuIMHOuAJuYuBofihFXbjMG5nKXDyvHSVawEBwczdOhQrl69CkDevHn55ptvaN++PQ0bNmTSpEm0adOGYcOG8e6772aqGTPpwYgp2bBhA+fOnWPHjh14enpmeN9CCCHEq6YNPYzribdAH4rBpSKh/pswOmT+S7x4Kl1Vxeeff07x4sXZv38///zzD2+++SYjR44kISGBvHnz8u233zJ37lx++uknOnXqlOVBbtiwgdatW0uhIoQQwirpQnaQ59jrqPWh4FmXiIDfpFDJQukqVs6dO8e7775L/vz5cXFxYcCAAYSHh3Pr1i3TOgEBAWzdupWGDRtmKpC2bdtSoUIFmjZtyvz5801jRuLj4zl37hwFCxZkxIgR+Pn5Ua1aNf73v//x5MmTTB0rN9q1awf9+vWkZcuGtGjRkHfe6cS3337JkyePszWOIUP6M3LkB9l2vNWrVxIYWCPbjpcdVq9eSfv2bWjSpC59+/bk+PF/XrjNhQvn+fDDwbz+eksaN65Dx45t+OabCTx8GJLqNg8e3Kd58/oEBtYgNDQ0xXU6dWqXbNnlyxdZvHg+sbGx6c4pKxgMBlavXsHbb3ekceM6vPFGK777bqLp9aioSBYvnk+/fu/SqlUj2rZtxkcfDeXixQvpPsapUycZNmwgzZvXp2XLhrz/fh9u3PjPbJ1Dhw7Qu/c7pt/z4sXz5aGuNs7+zmrcTnVDZYwl3qsVNPkdxS6vpcOyKenqBipRogQ7d+6kdu3aqNVqtm3bhp2dHYUKFTJbT6fTMXz48AwF4OXlxdChQ/Hz80OlUrF3716mT5/O/fv3GTduHKGhoej1ehYuXEjt2rWZPXs2ISEhTJ48mY8++oilS5dm6HjPS200s9FoG33IKhWsWLGUuXNn8dZb3ejTZyCKonDtWjC7du3k4cMQ8ubNvtsl//e/Ua9sBHnS8CKVCtJ/j1vOsnr1ShYsmM377w+hXDkffv55Mx9/PJyFC5dTpkzZVLeLjIygRIlStGvXgbx583L79i2WLVvEhQvnWLhwBTqdLtk2s2ZNx9HRiZiYGLPlGzeuo27dQAoVevqt8cqVy1y5colWrdpw+fIlli5dyJtvdsHBwSHTuWb0fH777ZccPXqY997rS8mSpXj8+BEXLpw3vX7//j1++WUTr732On37DkSv17N+/Rref783c+cuwcenfJr7//vvw4wc+SHt2rWnR49e6PV6zp07Q1xcnGmdM2dOM3r0/2jatAUDBgzh+vWrLFgwh5iYGIYM+SDNPEGFRqNCq7XNu4GS3vfZeQdJdrC/NhOnC2MAiCvSjTi/uei0Tmg0MS/YMmfL7uGc6SpWxowZw5AhQ9i6dSs6nY6oqCg+/fTTl7oQJalfvz7169c3/RwYGIi9vT3Lly9n4MDED1YANzc3Zs6cabqoOjs7M3ToUP7991+qVKmS6eO7uTmmuDw2VsPDh2qbuHisX/8Tbdq048MP/2daVr9+fXr2fA+j0fjSt0rGxsam+2+hXLnUP1CzyrMXQ40m8R2V088hJLYyrlixmC5duvHOOz0BqF69Bu+88xYrVy7hq6++TXXbgIAAs4eM1qxZk0KFCjF8+CCuXLlIlSp+Zuv/889R/vnnKO++25sffpiGVvv0fVC8eDG++GIMDRo0Qq9PYPbs6Vy6dIH33x+KVqtGrU76nWf8vXPnzh06dmzLpk3bTAP20/PhduTIX+zatZNVq36iVKmns2q3bNnK9P9ixYqxceMvODg8fc8HBATw5pvt2LRpLZ99Nj7V/ev1eiZN+opu3brz/vtDTcufvXYBLFu2gHLlvPnyy8QWnXr16gEKc+fOokePd8mXL+Wn6hoMoFarcHd3ypLrqjVL7Zqb4ygKnBoNFyYl/lz+I+z9v8P+/6fPt5k8rUS6ipWaNWuye/duTpw4QXx8PL6+vhQsWPCVBdW6dWuWLFnC+fPnqVWrFgDVqlUz+/aXdOG9fPnySxUr4eExGAzJ7/mPj4/7/2n2lRw9J4BKlfhYBA+PfKnmkTRDaGBgDQYNGk63bj1Mr61evZI5c2Zw8GBiV8Px4/8wbNhAJk+ezvbtv3D06BGqVvXH3T0PFy+eZ8WKtWb7PnhwP6NGfcTKlesoVao0Q4b0x8nJicmTp5v2tWjRSsqXr2DaxmAw0L59a1q3bsOgQYktddevX2PevB84ceIYBoMBf//qfPDBCIoUKWraLjo6kmnTvmPfvj+wt9fx2mvtcHV1B8jQOdTr9XTs2Ia2bd+gf/9BZq998cWn3LhxnSVLfkz3/rLKyZMniYyMpFmzFgAYDEYURUWTJs356acfSUgwZOjutaT5g+Li4s1+P3q9nilTJtGnT38cHZ3+f9nT90HNmnXw96/JmDEf8/DhQ6Kjo5kxYx4Av/zyMxMnJn7ot2rVFICCBQuxYcPWdMWU9F40GIwYDEY0GvX/55n2dj//vIXq1WtQrFjJVM+1nZ39/+fy9HWNxo4SJUoREhKS5t/IX3/9xb179+jQ4a0017t48SJt275htk7NmgH88MN0/vzzEK1bt022TdIpMxoVwsKiiYmxzS4jjUaNm5tjqtfcHMWox+nsMOxvrQAg2udL4kp9CKExtpVnGtzdHbN1TqB037rs6upKgwYNXmUsKXJ0dKRIkSKpvv6yvyyDwZjixSe1WTMVBaKjX+qQL8XJKWPNb4oC5ctXYMuWTRQqVIS6dQPJl+/lByp/991EWrRozcSJnVCr1ej1enbs2MbVq1coXfpp68mePbsoU6ac2bfdJFWrVsPLKz+7d/9mVqwcP/43T548plmzlgDcvn2LgQN7U7p0GcaM+QK1WsWKFUsYPvx9Vq/eaCpiJ06cwNGjhxk4cAiFCxdm06b1XLlyOdlxO3VqR8GChZg1a0GKuWm1Wlq3bsuOHdvo23eg6W8sIiKC/fv3MXhw2l2dev2L58pQq9UZ/tu9fv0aAMWLlwSedo2ULFma6OgoQkIekD9/gVS2TmQwGDAajdy5c5s5c2bi7V2eypXNW1XWrVuDWq2mfftO/PZb8nmU/v77CIsXz6dOnXpcvHgBBwcHhg8fxPvvD6FOnUDefbcPy5cv5vvvf8DZ2QWdLnPPvknKLz1dQGfPniYwsAHTp09hx46tJCToqVGjJh98MILChVO/fsTExHDp0kVatWqT5v7PnTuDu7s7586dZc6cmdy9e5uiRYvRu3d/mjZtYVovPj4Ordb8smpnl/j3+d9/19PME5Qc/+UoPVK75uYYhhjcTvfGPuRXFNREVvyB2CI94LmccnyeL5DdXe3pKlZOnTqFn5/fi1d8RmxsLDdv3qRcuYzfX759+3Y0Gg0VK1YEoFGjRuzevZv4+HjTB9Off/4JQPnyafczZyVFgbZtnfj7b022HfN5tWrp2bo1JkMFy4gRo/nkk/8xadJXABQqVIR69erTpUs3s3EHGREY2NCsOdxgMJA3rwe///4bAwYkFitxcbEcPLifnj17p7gPtVpNkybN2bv3dwYPHm5qFfj9998oXrwE3t6J53bp0oW4uroxbdps0+RZvr5+vPXW62zb9jMdO3bm+vVrBAX9wZgxn9G69etA4jfaLl3aZyq/du3as3r1Co4c+Ys6der9f1w7AWjevFWq2929e4fOnV9/4f579epHnz4DMhRTREQ4Op0Oe3vzboKk+YnCw8NfWKwMGdKf06dPAVC+fEWmTJlh9uH68GEIy5YtYuLE79BoUv47v3HjOp9//hWFChVm69YtDB36EZcvX+LKlUuUL1/R1Nrl41OBPHnypBmPoihmg0+TWvmMRuP/F32JM9hqNJo0W40eP37E9u3bKF26DF988TUxMbHMnz+LESOGs3z5T8kKiCQLF84hLi6WN998K804Hz16SExMLN9++yX9+r1PsWLF2bFjK59/PgZPz/z4+VUFoFixEpw/f9Zs27NnTwMQHh6W5jGE9VMlhOF2siu60EMoanvCKy8jPn/aha7IGukqVrp3705gYCA9evR44YMKHz58yJYtW1i2bBk9e/Z8YbHSp08fAgIC8Pb2BmDPnj2sW7eOnj174uXlZVrnl19+YfDgwXTv3p0HDx7w/fff06xZMypUqJDW7rOcSpXzRm6WKVOWlSvX8c8/Rzh69AgnTx5jw4af2L59K7NnL6BcOZ8M7zPpAzyJRqOhceOm7N37OwMGDAbg4MEDxMbG0Lx5y1T307x5S9au/ZF//z2Jn58/CQkJ7N+/jy5dupnW+fvvwzRt2gKNRmNqtXB1daVs2XJcuHAOgPPnz6IoCg0bNjFtp9VqqV+/IRs2mHdNpadLomjRYvj7V+fXX38x5frrr7/QsGFj3NxSf/yCp6cXixateOH+PT29Un3t+Q9wlUr1TOGQ0ge2YlrvRUaP/oyIiEhu377JypVL+fDDwcyZswhn58THZsyaNZ2aNWtRo0atVPfx5ptdki0rV86bcuW8X3j85504cYxhwwYmW/58kTlmzOe89lryO4+SKIqCohiZNGmaqUAqUqQoffp0JyjoD5o2bZ5sm127drJu3Ro++ugTihYtlmacSc/wGTLkAzp0SJyeoXr1mly5coWVK5fg5zcTgI4dOzNx4njWrVtDq1avce3aNRYsmINGo7HZafRzC1XcffIc74g28jRGrRvhfj+R4BFo6bByjXQVK7/++iuTJk2id+/eeHl5UbNmTSpWrEi+fPmwt7cnNDSUmzdvcvLkSU6fPo2bmxtDhw6lS5fkF7XnlSpVig0bNnDv3j2MRiMlS5ZkzJgx9OjxdNxEkSJFWLZsGd9++y1Dhw7F0dGRli1bMnLkyMxnngkqFWzdGpOjuoGS2NnZUadOIHXqJL65jhz5i5EjP2Dp0sRv0RmVN2/y2/KaN2/Fpk3rOXfuDBUr+rJ792/4+lamYMFCKewhUfnyFSlatDi///4bfn7+HD58iMjICFMXEEBoaCjr1q1h3bo1ybbX6RJbGR4+fIhWq8XNzc2s6fVl7nRq1649EyeOJzQ0lIcPQ7h48TyDBg1Lcxs7OzvKln3xh3ZaH1zPf4BXrVqNWbMW4OrqRnx8HHFxcWi1TwfvRUQkPhX82RmgU5PUhVSpki/Vq9fkzTfb8vPPm+nWrQdnzvzLvn17WLBgGREREQCm24+joiJxcHBINvgzvWNRUlO+fAWz4u7hw4eMGvUR3347FU9PT9OYlRe1ALq6ulGoUGGzlhwfn/K4uLhw/frVZOv//fdhvvlmPN269aBjx84vjNPVNbFArV69pmmZSqWiWrUa7N//h2lZ69ZtCQ6+wuzZ05k583vs7Ozo1asf69atwcMj5cG1wvqpo6+S53h7NDHXMeryE1ptMwbXypYOK1dJV7FSvHhxZs+eTXBwMGvWrCEoKCjZM4EcHByoWrUq48ePp127dineCpmSsWPHpms9X19fVq1ala51XyWVCpydLR3Fy6tduw5lypTjv/+umZbpdDr0+gSz9SIiwlPcPqVv8b6+VShYsBB79uyiRImSHDny5wvHd0Bi68rmzRv44IOP2b17Fz4+FShWrLjpdTc3d+rUqZfih4qTU+IAUE9PT/R6PeHh4Tg5PX245svMI9OwYROmT/+OXbu2c+fOHQoVKkK1amnP2ZIV3UDPf4An5ViyZCkA/vvvmqmLFOD69as4OTnj5ZX/hcd9Vr58nnh6enH79k0Abtz4D71eT+/e3ZOt26VLe5o2bc748d9k6Bgv4uTkTPnyT3O5e/cOkNgaWKhQ4XQ/yLBEiZLEx8en+Nrzf6vnzp1hzJiRNG7cjPffT7v4TJL0u3+eoihmhadKpWLo0A/p1asf9+7dpUCBguj1ehYsmEOlSvLhlhNpIv4lz/GOqOMfYHAsSWi1LRidko/BE69WugfYApQpU4axY8cyduxYHj9+zIMHD4iNjSVv3rwULlwYO7vMDaQTr9ajR49wdzdvCYmLi+XBg/tmA1+9vPKbFS8A//xzJN3HUalUNG3agt9+206pUmUwGAw0aZK8+f15zZq1ZOnShRw6dIA//zyQ7EO8Ro1aXLsWTLlyPqmOoyhfviIqlYqgoL2mMSt6vZ4DB4LSHf/zdDodLVu+xi+/bOHJk0d07vz2C7tasqIb6PkP8CS+vlVwcXFhz57fTcWKwWBg797d1KlTN8PPsbp37y4hIQ9MA1Br167DzJnzzNY5cuQvfvxxOd98M4WiRYuntJtktNrE60B8fNwL1sw6devWZ/HieTx58sTU6nfhwjkiIyPNWrquX7/GiBHDqVzZjzFjPk/376xWrTpoNBr++ecIJUqUBBILlRMn/qFs2eRd3S4uLqblixbNo1Chwml2rQnrZPf4IG6nuqLWh6N3qUxotU0o9mmPCxOvRoaKlWd5eHik+iwfYV26d3+LunXrU6tWHTw9PXn4MIQNG9YSFhZK585vm9Zr1Kgp69evoUKFShQrVoKdO3/l0aNHGTpW8+at+PHH5SxaNI/q1WumqxumRImSeHv7MG3aZGJjY83urgDo02cAffv25KOPhvL66x3w8PDg8eNHnDhxHD+/qjRv3opSpUpTv34jpk37npiYOAoVKsSmTetNAzaf1aVLewoWLMSMGXNfGFu7dh1Md8ekNWYiiZ2dXYqFRlbQ6XT07NmHBQtm4+HhQblyPmzduoU7d24zfvzTmVrv3btLly7tee+9vvTq1Q9IvHvL3T0P5ctXxMXFhRs3rrNmzSo8PPLRtu0bQGJLy/N3it27l/iw0MqVq75wsGySkiVLArBp03rq12+Eg4NDmhPWZYU33ujAxo1rGTnyA959tzexsbHMnz8Hb+/yBAYm3sX45MljPvpoCFqtlm7dephNGKfT2ZkGdAM0bFibVq3aMHr0OCCx5a5jx7eYN282iqJQrFgJtm/fyrVrVxkz5gvTdufOneHkyeOUK+dNXFwcBw/u57fftjNlysxUC21hnXQPtuF2uhcqYxzxeeoRXvUnFDt3S4eVa2W6WBE5R58+AzhwYD+zZk0jNPQJ7u55KFOmHDNmzDXr1njvvb48efKYJUsWolaref31DpQtW465c39I97HKlk28TfnatavJ5ihJS7NmLZkzZ6bpduZnFS1ajIULl7Nw4VymTv2WmJgY8uXzxM/PnzJlnn6rHT16HNOnT2bu3MTJA1u1akuVKv7Mnz/LbH8GgyHd05+XKlWaYsWKU6RI0RfeaZMd3n67O6Cwfv0aHj9+TOnSZZkyZYZZMZA0QPfZQq1ChUr88stmNm1aT0JCPAUKFKROnUB69uyFu3ueLI3R27s8vXv3Z9u2n1m9egX58xd46bEtL+Ls7MLMmfOYMWMK48ePRa1WExBQl6FD/2fqprl27SoPHtwH4IMPzP82n58L5vnfH8DgwcNxcnJi1arlhIWFUqpUaSZNmmY2862dnR379u1l6dJFAFSs6MsPP8zH1zfzc0GJ7OdwewUu54ahwkicVxvCKy8FjW1P1mftVIpiqxOTp8+TJ1Ep9oknJMTz6NFd8uUrZJonIadKb7+/LcjqXG/fvkXXrh348stvadSoaZbt92XllnOaW/I0GhN48OCOTVxvUqPVqsmb1znVa65VUBQcr0/D5coXAMQU7klkhemgTv/3+hyRZxbw8HDO1kcnSMuKECkICwvl5s0bLFmykIIFCxEYmLkHdAohcgjFiPOlsTjdSGyJjS75P6LKjsv+h+CIFMmN/0Kk4NChA7z/fh/u3r3NZ59NSHVSMSGEDTAm4Hp2gKlQifSeSFS5z6VQsSJyBRYiBa+91i5dA2qFEDmcIQq3f9/F/uEuFJWGiIpziCv89ou3E9lKihUhhBC5kirhMe4n3sIu7CiK2pHwKsuJ90r9cRrCcjLUDXT8+HG2b9/OpUuXUnz9/v37zJo1K8XXhBBCCGuhjr1Dnr9bYxd2FKM2D6HVf5ZCxYqlq2UlPj6egQMH8tdff6EoCiqViubNm/PVV1+ZPSfl3r17zJ49myFDhryygIUQQoiXoYm6jPvxDmhib2CwL0RYtc0YXF7N/Egia6SrZWX58uUcO3aMESNGsGrVKgYNGsT+/fvp2rUrDx48eNUxCiGEEFlCG36CPH+3QBN7A71TGUJr7pJCJQdIV7Hyyy+/0L9/f3r37k2NGjUYOnQoP/30E9HR0XTv3p179+696jiFEEKIl2L3aB/u/7RBnfCIBFd/QmvuwuhYwtJhiXRIV7Fy69YtatQwf4Bb+fLlWbVqFXq9nu7du3P79u1XEqAQQgjxsnT3N+N+ohNqQyTxHo0Iq7ENRZf6M7qEdUnXmBVnZ2eio6OTLS9atCgrV66kR48e9OjRg48++ijLAxQvb+HCeSxevABIfNigk5MTBQsWws/Pn44d30r2RNkhQ/rj5OTE5MnTX/rYq1evZM6cGRw8+M9L7ys73bp1kzVrVnL27BmuXQumePESrFy5Ll3b7tmzi717f+fs2TM8fBjCoEHD6datR7L1AgOTP8HZwyMfv/zyW7LlixfPp1Chwslup163bjXFihWnTp3AdGaWtaZPn8KGDT/RsWNnPvroE9Pyr7/+gh07tqW4zYABQ+jR471U95natlOmzCQgoK7p506d2pmeXfSsPXsOYW9vn4EshK1zuLkIlwv/Q4VCXP72hFdeCGr5G8lJ0lWslCpViqNHj9K4ceNkrxUpUsRUsHz66adZHqDIGvb29syYkfhE3ZiYKIKDr/DLL5vZunULo0Z9RsuWr5nW/d//RmXrNMrW6Nq1YP766xAVK1ZCUYwpPhAxNX/8sYc7d25Tr159fv55U5rrdurUhWbNnt6B8OyTy2/evMGJE8do1669aZler2fdujW0b98BJycX1q1bQ926gRYpVoKDr/Drr7/g7Oyc7LX33uvLG2+8abZs795drFu3xqzgSE3hwkUYN+4rtFoVen3iE0GeL6oh8eGbXbt2N1um09nmdPUiExQFp6vf4nz1GwBiivYhsvwUUMlDJXOadBUrdevWZeXKlQwfPhwHh+QPcypSpAirVq2iZ8+e0h1kpdRqNb6+lU0/16wZQIcOnRk58gO+/fZLfH2rUKRIUSDx4X25Xb16DahfvxGQ+E3/woVz6d52woRvTA/Pe1Gxkj9/QbPz8ix3d3euX7/KsGEDyZvXg0ePHrJly0bq1g1Ep7NLcZuM+PrrLwD49NMvMrX9tGmT6dr1nRRbQYoUKWr6e0oyb94PlCxZmnLlvF+4b3t7e3x9K7/w2UAeHh6p/v5ELqcYcbk4AsebCwGIKj2K6NKjZVbaHCpdX5979OjB8uXL01yncOHCrFu37oXrCethb2/PBx+MICEhgW3bfjYtHzKkPyNHfmC27vXr1xg16iNatmxIs2aBjBgxnNu3b5mtExUVyZdfjqN58wa0bduMOXNmpPvpxs/S6/W8/npLFiyYk+y1L774lN6938nwPjMqqdjI7m2f5ebmzrBh/6NXr34cPBjEvn17GT16HO+91xcHB0dTN8imTesJDKxBYGANtm9/tU83TrJr1w7u3LnNO++8m671Q0Ie8O+/J2nRQuaxENnAGI/r6d443lyIgooIn++ILjNGCpUcLF0tKy4uLpQrV+6F63l4eFCrVq2XDkpkn1KlSuPllZ8zZ/5NdZ3bt28xcGBvSpcuw5gxX6BWq1ixYgnDh7/P6tUbTc3u33wzgSNHDjNw4BAKFy7Mpk3ruXLlcrL9derUjoIFCzFr1oIUj6fVamndui07dmyjb9+Bpg//iIgI9u/fx+DBw9PMSa/XvzBvtVqdZUXFy/jxx2XMnz8LR0dHatWqw6BBwylYsCCQmO+KFUu4cOEcgYENcXZ24ZtvJlC/fkPefvsdJk78jhEjhlO5clVTV8jzrRmvQnR0FLNnz2DYsI9SbGlNye+//4bRaKRZs5bpWv/27du0atWI2NhYSpcuy3vv9aVBg0bJ1tu1aydbt25Bq9Xi5+fP++8Po0yZshlJR9gafSTup95B9/gPFJUdEb7ziSvYydJRiZeU4en2z507x6FDh7h79y4qlYqCBQtSr149KlbMJfepKwoYkw82zjZqpyz/dpA/fwEeP36U6utLly7E1dWNadNmmwYu+vr68dZbr7Nt28907NiZ69evERT0B598Mpa2bd8AEruaunRpn6mY2rVrz+rVKzhy5C/q1KkHwO+/7wSgefPUv53fuXOHjh3bvnD/vXr1o0+fAZmKLau0atWGunXr4+HhwdWrwSxfvohBg/qwbNka3NzcePLkMUWLFmPQoGEsWbKAQoUK87//fcK6dWuIi4vD27s8dna6dHeFGAwGFEUx/Zz0/2eLO5VKhUaTdn/+4sULKFq0GE2btkh3rrt378TXtwqFCxd54brlyvlQvnxFSpUqTUxMFBs3rmfMmI/58stvady4mWm9wMAGVKzoS4ECBblz5zbLly9h0KA+LFnyY7YUbcL6qOIf4X7iTezCj6NonAnzW0VCvqaWDktkgXQXK48fP+aTTz7h4MGDZhc8gKlTp9KgQQO++eYbPDw8sjxIq6Eo5Pm7BXZhRywWQkKeAEJr/JalBUvSrMSp+fvvwzRt2gKNRmP6YHN1daVs2XKmsRznz59FURQaNHg6CFur1VK/fkM2bFhrtr8NG17cVVG0aDH8/avz66+/mIqVX3/9hYYNG5vNmvw8Ly8vFi1a8cL9e3pa/pbFsWPHm/5ftWo1qlSpSp8+3dm6dTPvvPMuxYuXoHhx8zkgtFot3br1eOFYjpQMH/4+J08eT7Z8585fTf8vWLBQmufn2rWrbN68nvnzl6b7uP/9d51Lly7y4Ycj0rX+W289fYicVqumTp36DBzYm8WL55sVKx988HR/fn7+1KwZwDvvvMmaNav4+ONR6Y5P2AZ1zE3cj7dHG30Zo50HYf7r0bvXtHRYIoukq1iJiYnh3Xff5dq1a3Tu3JlmzZpRtGhRFEXh9u3b7N69m02bNtGrVy/Wrl2b7qbhHMkG+zxDQh5QrFjxVF8PDQ1l3bo1rFu3JtlrOl3iuX748CFarTZZIZE3b+aL13bt2jNx4nhCQ0N5+DCEixfPM2jQsDS3sbOzo2zZFw/gtIYuoOeVLVuOYsVKcPHihWSvZUUr0MiRY8ymIFiyJHHgYe/e/UzL7OzSvpPmhx+m0bhxUwoWLExERAQARqORhAQ9ERERODs7J/vd7tq1A41GQ5MmzTMVt1qtplGjJsyZM5O4uFjs7VO+vnh6elKlSlUuXjyfqeOInEsTeSFx+vy42xgcihLmvxmDi4+lwxJZKF3FyooVK7h58yYrVqygWrVqZq+VKVOGBg0a8Prrr9OvXz9WrFhB//79X0mwFqdSJbZq2FA30NWrwYSEPKB169S7Ttzc3KlTpx4dO3ZO9pqTkxOQ+EGh1+sJDw83K1iePHmc6dgaNmzC9OnfsWvXdu7cuUOhQkWoVi353CTPykndQClTXrxKJhUvXtLsZ3d3dwDKl09/F+6NG9c5evQuv/22w2z51q2b2bp1Mz/+uIESJcyPs3v3b9SoUfulCtfnW3Nfdj1hO7ShR3E/2Rl1whP0zj6EVduM0UG6AW1NuoqVHTt28O677yYrVJ5Vo0YNevTowfbt2223WIHEQkGTfF6JnCguLo7p079Dp9OZzeXxvBo1anHtWjDlyvmkOp6hfPmKqFQq9u//wzRmRa/Xc+BAUKbj0+l0tGz5Gr/8soUnTx7RufPbaXZXQc7qBnre5csXuXnzBq+99nq6t9Fq7YiPj3+FUZn74ouJxMfHPbfsUypVqkznzl0pUKCg2Wtnz57h9u1b9OrVj8wyGo3s27eHUqVKp9qqAvDwYQinT58ymzNI2Da7h7/jfqoHKmM0Ce41CKu6HkWXz9JhiVcgXcXKf//9R0BAwAvXq1OnDitXrnzpoETWMxqNnDlzGoCYmGiuXk2cFO7OnduMGfM5hQoVTnXbPn0G0LdvTz76aCivv94BDw8PHj9+xIkTx/Hzq0rz5q0oVao09es3YubMqcTHx1OoUCE2bVqf4mRqXbq0p2DBQsyYMfeFcbdr14F169agVquTzd6aEjs7uwy1FKQmNjaWv/46CMC9e3eJiorijz92A1C1anXy5s0LJN4BtXPnrwQFPR3HdO3aVa5fv2r6+erVK/zxx24cHBxN429Wr17JnTu38fevRt68Hly9eoUVK5aSP3+BNAvH55UsWZJjx/7h778P4+rqRqFChXF3z/OS2acupYG8Op0OLy+vFFu9fv99J/b29mZjmZ61dOlCli1bxNq1WyhYsBD37t3l66+/oFmzlhQpUpTo6Eg2blzPhQvn+eqryWb7/euvQwQE1MXT04s7d26xcuUy1GpNsknihG2yv7sO17MDUSl64vM1Jcxvlc18kRTJZfhuoKy2adMmRo8enWx5v379+Pjjj5MtP3PmDJ07d8bBwYETJ05kR4g2IS4ujoEDe6FSqXB0dKJgwYJUr16TiROnJGu2f17RosVYuHA5CxfOZerUb4mJiSFfPk/8/PwpU+bpLe2jR49j2rTJzJ07E51OR6tWbalSxZ/582eZ7c9gMKR7/pVSpUpTrFhxihQpSv78BTKcd2Y9efKYzz4zH6SZ9PPMmfPImzfxg9loNCbLZe/e31m6dKHp5507f2Xnzl/NBq8WL16CoKC97Nmzi+joKPLkyUudOvXo128Qrq6u6Y6zf//BfP/9t3z66SdER0cxZszn6SrqsoPBYOCPP36nXr0Gpu7C5yX9/pK6b5ycnHB2dmbZskWEhj7Bzs4OH58KTJkyk9q165i2K1SoCA8e3GfmzO+JiIjA1dWVatVq0rfvgHTdcSRyNscbc3G5mPh4h9iCnYioNA/UMnOxLVMp6ejk7dChAw0aNODDDz9Mc72pU6dy8OBBNm1Ke9bOZyUVK4sWLTK7SBcoUIBChQqZrasoCl27duXWrVtER0dnSbHy5ElUindVJCTE8+jRXfLlK/TCQYfWLjN3jliL27dv0bVrB7788lsaNXrxLYg5OdeMkDxti9GYwIMHd2ziepMarVZN3rzOqV5z00VRcAr+EudrUwCILjaAKJ9JoLKeAfNZkmcO4OHhnK2PZUnXkVq3bs2KFSvSLA6OHz/OypUradUqczNUVqpUiapVq5r+PV+oAGzcuJEnT57w5ptvprAHYUvCwkI5c+Zfvv9+EgULFiIwsKGlQxJCWJJiwOX8cFOhElXmM6J8JltVoSJenXR1A/Xo0YOtW7fSo0cPOnfuTNOmTSlaNHG09a1bt9i9ezcbNmygdOnS9OzZ85UEGh4ezvfff8/EiRM5c+bMKzmGsB6HDh3gm28mULRoMT77bAJarcV7LIUQlmKIxe1MX+wf/IKCmsgKU4kt2tvSUYlslK5PAEdHR5YtW8bIkSNZs2YNP/30k9nriqJQr149Jk+enOk5Vtq2bcuTJ08oXLgwb731Fn379jW782T69OlUqlSJxo0bS7GSC7z2WjurGXshhLAclT4ct5Pd0D3Zj6LSEV55MfEF3rB0WCKbpfvrar58+Vi8eDFnzpzhzz//5M6dO0DiAwzr1q2Lr69vpgLw8vJi6NCh+Pn5oVKp2Lt3L9OnT+f+/fuMGzcOgPPnz7NhwwY2b96cqWOkJbU+N6PRNiZ/S7rTV6VKfFKALcstuUqetuXp3fgqNBoVWq1tdmskXWszMs5BFXcfl2Md0YafQtG4EFn9J4z5Gln+zpA0ZCbPnCi750fN8Dn39fXNdGGSkvr161O/fn3Tz4GBgdjb27N8+XIGDhyIl5cXEyZMoFu3bpQpUybLjpvEzc0xxeWxsRoePlTbzMXD1t84z8otuUqetsNgALVahbu7k23PAE7q19xkIq/BgZYQeQXsvVA13oGrR/VXG1wWSneeIl1eqkCNj49n48aNXL58mfz589OpUyc8PT1fOqjWrVuzZMkSzp8/z99//01wcDBTpkwhPDwcSLwNFxLHsdjb25serpcZ4eExGAzJR2zHx8djNBrR642o1Tl3RLdKlXixNxiMNv3tFHJPrpKnbUn8hqpgNCqEhcUQE5O+2/pzGo1GjZubY6rX3GepI87g+nd71HH3MDgWJ7LmzxhV5eBJVDZFm3kZyTMnc3d3zNbHlqSrWFmwYAG7du1iw4YNpmV6vZ63336bc+fOmeZI+PHHH9mwYQMFCmTdfBhXr14lLCyMJk2aJHutZs2aqc7Hkl4GgzGV28sS27ji4+PQ6TJfDFla0kXeli/2SXJLrpKnbVGUpC9gCmD7t2qnfs1NpH3yF64nu6DWh6J3qUiY/2aM9oUgh/1eXpRnTpfd78t0FSsHDhygcmXzmSvXrl3L2bNnee211+jfvz/BwcF8/vnnzJ8/3zTWJLO2b9+ORqOhYsWKlClThlq1apm9vnnzZrZv387ChQspXDj1mVdfhlqtwdHRhcjIJwDodPYvnOrdWhmNKgwGG7/i/7/ckqvkaRsURSE+Po7o6DCcnV2t8gGb2UkXsgO3f99FZYwlIU8AYVXXotjltXRYwgqkq1i5fv06nTubP8Ru9+7duLq6MnHiRBwcHChfvjzXrl1j69bUHy+fkj59+hAQEIC3d+KTcvfs2cO6devo2bMnXl6Jz29Juk06ydGjR9FoNNSuXTtDx8ooN7fEB68lFSw5lVqtTnHae1uUW3KVPG1LvnweODi42XRh9iL2d37E9dwQVIqBOM9WhFdZBpqUZz4WuU+6ipXQ0FCzSdqMRiMnTpygbt26ZoPBqlatyoIFCzIUQKlSpdiwYQP37t3DaDRSsmRJxowZQ48ePTK0n1dBpVLh7p4PV9e8GAx6S4eTKRpN4qC9sLBom78Q5pZcJU/bYm9vR758bjx5EsWrfOq2NXO8PhOXy2MBiC3UjYiKP4DazsJRCWuSrmIlT548hIWFmX6+dOkSsbGxye4Ksre3T/WpvKkZO3ZshtYHGDp0KEOHDs3wdpmlVqtR59DnTmi1ahwcHIiJMdh0/ynknlwlT9uSq7t+FAXny+Nw+m8GANElhhJV7kuZlVYkk66/CG9vb7Zt22b6effu3ahUqmRjSW7cuGHquhFCCCFSZdTjem6QqVCJLPclUd5fS6EiUpSulpX33nuPfv36mcaR/Pbbb5QrV44aNcwfCb9//37Kly//SgIVQghhIwwxuP37HvYPd6CgJqLiLOKKdLd0VMKKpauErV+/Pp9//jm3bt1i79691KpVi9mzZ5utExISwuHDh80meBNCCCGepUoIxf14h8RCRe1AuN9qKVTEC6V7Uri3336bt99+O9XXvby8OHLkSJYEJYQQwgbF3MXlSCu0EWcwat0Ir7qWhLz1LB2VyAEy1DkYGxtL/fr12bt376uKRwghhA1SRwXDrnqJhYouP6E1dkihItItQ9PtOzg4EBcXh6OjPPNACCFE+mjDT+F6oiPEh2BwLEVotS0YnUpZOiyRg2R42HVAQAB//fXXq4hFCCGEjbF7fAD3f15DHR8CeasSUWe3FCoiwzL8IMOBAwcydOhQdDodLVq0wMvLK9k09Hny5Mmq+IQQQuRQuvu/4Ha6NyolngSP+tg12YoSpc1xz/kRlpfhYqVjx44AzJo1K9kdQUnOnz//clEJIYTI0RxuLcPl/AeoMBKXvx3RVZeSV+cOUdb/5GRhfTJcrAwePDjHPtBPCCHEK6YoOF3/HucrEwCIKdyTyArT0Wpy5izgwjpkuFjJzmnuhRBC5CCKEedLo3G6MReAqFIfE13mM5AvuOIlZbhYEUIIIZIxJuB69n0c7q0DINL7G2JKDLZwUMJWSLEihBDi5RiicDvVE/tHv6OotERUmktcoS6WjkrYEClWhBBCZJoq4THuJ97CLuwoitqRcL+VxHu2sHRYwsZIsSKEECJT1LF3cD/eHm3UBYzaPIT5r0efp7alwxI2SIoVIYQQGaaJuoz78fZoYm9isC9MWLXNGFwqWDosYaOkWBFCCJEh2rBjuJ/ohDrhEXqnsoRV24LRsbilwxI2TIoVIYQQ6Wb3aC/up95BZYgiwa0aYf4bUHSelg5L2LgMPxtICCFE7mR/bxPuJzqjMkQR79GYsOpbpVAR2UKKFSGEEC/kcHMhrqd7oVISiC3QkTD/dShaV0uHJXIJ6QYSQgiROkXB6eo3OF/9FoCYon2ILD8FVBoLByZyEylWhBBCpEwx4HJhBI63FgEQVXo00aVHyfT5IttJsSKEECI5YxyuZwbgcH8TCioiy08htlg/S0clcikpVoQQQphR6SNwO9Ud3eM/UFR2RPguIK7gm5YOS+RiFi9WNm3axOjRo5Mt79evHx9//DEGg4ElS5YQFBTElStXMBgMeHt7M2TIEOrUqWOBiIUQwnap4h/ifqITduHHUTTOhPn9SEK+JpYOS+RyFi9WkixatAhX16cjywsUKABAbGws8+fPp3379vTp0wetVsvmzZvp1asXc+fOpXHjxpYKWQghbIo65gbuxzugjb6M0c6DMP+N6N2rWzosIaynWKlUqRIeHh7Jljs4OLBnzx7c3d1NywIDA7l+/TpLliyRYkUIIbKAJvI87sc7oIm7g8GhWOL0+c7elg5LCCAHzLOi0WjMChUAlUpF+fLlefDggYWiEkII26ENPUKev1uiibuD3rk8oTV3SaEirIrVFCtt27alQoUKNG3alPnz52MwGFJd12g0cuLECcqUKZONEQohhO3RPdxFnmOvo9aHkuBei9CaOzE6FLF0WEKYsXg3kJeXF0OHDsXPzw+VSsXevXuZPn069+/fZ9y4cSlus3LlSq5du8aECRNe+vgajdXUa69EUn62nifknlwlT9tiyTx1t9fidHoAKkVPglcLIquuRKN1fiXHkvNpW7J7qh2VoihK9h7yxSZNmsTy5cvZt28f+fPnN3vt6NGj9O7dm549ezJy5EgLRSiEEDnchRlw/IPE/5d8BwKWgtrOoiEJkRqLt6ykpHXr1ixZsoTz58+bFSsXLlxg0KBBNGvWjBEjRmTJscLDYzAYjFmyL2uk0ahxc3O0+Twh9+QqedqWbM9TUXC4PB7H4CkAxJYYREz5byEsHoh/ZYeV82lb3N0dUauzr/XIKouVlNy4cYO+fftSsWJFJk+ejCqL2qAMBiN6ve3+QSXJLXlC7slV8rQt2ZKnYsDl/Ic43l4GQGTZz4kp+REYALLndyzn0zZkd5+MVRYr27dvR6PRULFiRQBCQkLo3bs3np6ezJkzB51OZ+EIhRAihzHE4namL/YPfkFBTWSF6cQWfc/SUQmRLhYvVvr06UNAQADe3om3ye3Zs4d169bRs2dPvLy8iI2NpW/fvjx69IhRo0Zx5coVs+2rVq1qgaiFECLnUCWE4XaqG7onB1DU9oRXXkJ8/naWDkuIdLN4sVKqVCk2bNjAvXv3MBqNlCxZkjFjxtCjRw8AHj58yIULFwAYPHhwsu0vXryYrfEKIUROoop7gPuJjthF/ItR40p41TUkeDSwdFhCZIjFi5WxY8em+XrRokWlIBFCiExQR18jz/H2aGKuYdR5Eea/Cb2bn6XDEiLDLF6sCCGEyHqaiNO4H++IJv4+BocS/z99fllLhyVEpkixIoQQNsbuyZ+4neyCWh+G3sWXsGqbMNoXtHRYQmSaFCtCCGFDdA+243b6PVTGWBLy1CGs6loUuzyWDkuIl2Lb8wELIUQuYn/nR9z+fQeVMZY4z9aEVtsihYqwCVKsCCGEDXC8Ph23s++jUgzEFn6HcL8fQeNo6bCEyBLSDSSEEDmZYsT58jic/psJQHSJ4USVm5D9T5oT4hWSYkUIIXIqYwKu54bicHc1AJHlviSm5HALByVE1pNiRQghciJDDG7/vov9w50oKg0RFWcRV/gdS0clxCshxYoQQuQwqoQnuJ/sil3oXyhqB8KrLCfeq7WlwxLilZFiRQghchB17F3cT3REG3kWo9ad8KprSchb19JhCfFKSbEihBA5hCbqCu7HO6CJ/Q+DriBh1TZhcPW1dFhCvHJSrAghRA6gDT+J+/GOqBMeoncsTVj1LRgdS1o6LCGyhRQrQghh5eweB+F2shtqQwQJrn6E+W9Esc9v6bCEyDYyKZwQQlgx3f2fcT/+JmpDBPF56xNWfZsUKiLXkWJFCCGslMOtpbj9+y4qJZ64/K8ntqjYuVs6LCGynRQrQghhbRQFp6vf4Xp+OCqMxBR5j/Aqy0HjYOnIhLAIGbMihBDWRDHifHEUTjfnARBVagTRZcbK9PkiV5NiRQghrIUxHtez7+Nwbz0AkT6TiCn+voWDEsLypFgRQghroI/C/eQ76B7tRlFpiag0j7hCb1k6KiGsghQrQghhaXGPcD3aFm3Y3yhqJ8L8VpLg2dzSUQlhNaRYEUIIC1LF3IJDHdCGn8eozUOY/3r0eWpbOiwhrIoUK0IIYSGayIu4nWgPsbcxOhQh1H8zBpfylg5LCKsjxYoQQliANuwf3E90Qp3wGNx8CK+2BYNdEUuHJYRVknlWhBAim9k92kOeY+1QJzxG714dmh1EcSxm6bCEsFoWL1Y2bdqEj49Psn9TpkwxWy8oKIj27dtTuXJlmjdvzo8//mihiIUQIvPs723A/cRbqAxRxHs0JqLWr+DgaemwhLBqVtMNtGjRIlxdXU0/FyhQwPT/EydOMGjQIN544w1GjRrF8ePH+eqrr9DpdHTu3NkS4QohRIY53JiPy8WRqFCILdCRCN/5aLWOlg5LCKtnNcVKpUqV8PDwSPG12bNnU7FiRSZOnAhAQEAAd+/eZcaMGbz55puo1RZvIBJCiNQpCk5XJ+J8dRIAMUX7Eln+O1BpLByYEDmD1X/Kx8fHc/jwYdq0aWO2vF27doSEhHDu3DkLRSaEEOmgGHC58JGpUIkqPYbI8t9LoSJEBlhNsdK2bVsqVKhA06ZNmT9/PgaDAYAbN26QkJBA6dKlzdYvW7YsAMHBwdkeqxBCpIsxDrd/e+F4azEKKiLKTyW6zCh5zo8QGWTxbiAvLy+GDh2Kn58fKpWKvXv3Mn36dO7fv8+4ceMICwsDwM3NzWy7pJ+TXs8sjcZq6rVXIik/W88Tck+ukmcOoY/A5eTb2D3ah6KyI8pvMfpCHZNddHN8nukkedqW7K63LV6s1K9fn/r165t+DgwMxN7enuXLlzNw4EDTclUqv5nUlqeXm1vuGNyWW/KE3JOr5GnFYh/Avrbw+BhoXVA12IxLwWZpbpIj88wEyVNkhsWLlZS0bt2aJUuWcP78eYoUSZwk6fkWlPDwcCB5i0tGhYfHYDAYX2of1kyjUePm5mjzeULuyVXytG7q6P9w+ac9mqjLGO3yEVlzMwb7avAkKsX1c2qeGSV52hZ3d8dsvbnFKouVZxUvXhw7OzuuXr1KgwYNTMuvXLkCQJkyZV5q/waDEb3edv+gkuSWPCH35Cp5Wh9N5DlcjndAE3cXg0NxwqptxuBcDtIRf07K82VInrZBUbL3eFbZqbZ9+3Y0Gg0VK1ZEp9MREBDAjh07zNbZtm0bXl5eVKxY0UJRCiHEU9rQI+T5uxWauLvonSsQWnNXYqEihHhpFm9Z6dOnDwEBAXh7ewOwZ88e1q1bR8+ePfHy8gJg8ODBdO/enbFjx9KuXTuOHz/O+vXrmTBhgsyxIoSwOF3Ib7j92xOVMYYE99qE+a9FsUt53ighRMZZvFgpVaoUGzZs4N69exiNRkqWLMmYMWPo0aOHaR1/f3/mzJnD1KlT2bJlCwULFmTs2LEye60QwuLs7/6E69n3USkG4jxbEF5lBWicLB2WEDbF4sXK2LFj07Vew4YNadiw4SuORggh0s/xv9m4XBoNQGyhrkRUnA1qOwtHJYTtsXixIoQQOY6i4HxlPE7XpwIQXXwIUd5fgUq6pYV4FaRYEUKIjDDqcTn/AY53VgAQWfYLYkp+KLPSCvEKSbEihBDpZYjB7XRv7EN+RUFNZIUZxBZ919JRCWHzpFgRQoh0UCWE4XayK7rQQyhqe8IrLyU+f1tLhyVEriDFihBCvIAq7j55jndEG3kao9aNcL+fSPAItHRYQuQaUqwIIUQa1NFXyXO8PZqY6xh1+QmttgmDaxVLhyVEriLFihBCpEIT8S95jndEHf8Ag2NJQqttwehU2tJhCZHrSLEihBApsHtyCLeTXVDrw9G7VCas2kaM9gUtHZYQuZIUK0II8Rzdg19xO/0eKmMc8XnqEl71JxS7PJYOS4hcS2YwEkKIZzjcXonbqXdQGeOI82pDWLXNUqgIYWFSrAghBICi4HhtKq7nBqPCSEzhHoRXWQkaR0tHJkSuJ91AQgihGHG+NBanG7MAiC75EVFlP5dZaYWwElKsCCFyN2MCrueG4HB3DQCR3hOJKTHEwkEJIZ4lxYoQIvcyROP277vYP/wNRaUhouJs4gp3s3RUQojnSLEihMiVVAlPcD/xFnZhR1DUjoRXWUa8V2tLhyWESIEUK0KIXEcdewf3Ex3RRp7DqM1DmP869HkCLB2WECIVUqwIIXIVTdRl3I93QBN7A4OuIGHVNmNwrWTpsIQQaZBiRQiRa2jDT+B+/E3UCQ/RO5UhrNoWjI4lLB2WEOIFpFgRQuQKdo/24XaqG2pDJAmuVQmrthFF52XpsIQQ6SCTwgkhbJ7u/hbcT3RCbYgk3qMhYTW2SaEiRA4ixYoQwqY53FqC27/volLiicvfnjD/DShaN0uHJYTIAClWhBC2SVFwujoJ1/MfoEIhpkgvwqssBbW9pSMTQmSQjFkRQtgexYjLxZE43lwAQFSpkUSX+VSmzxcih5JiRQhhW4zxuJ4ZgMP9jQBE+kwipvj7Fg5KCPEyrKobKCoqigYNGuDj48Pp06dNy/V6PQsWLKBVq1b4+fnRpEkTvvrqK8LDwy0YrRDC6ugjcT/5Fg73N6KotIT7LpZCRQgbYFUtK3PmzMFgMCRbPnv2bBYsWMDQoUOpWrUqwcHBTJs2jVu3bjFv3jwLRCqEsDaq+Ee4n+iEXfgxFLUTYX4rSfBsbumwhBBZwGpaVoKDg1m9ejVDhw5N9tq2bdto27YtAwcOJCAggHfeeYcBAwYQFBREdHS0BaIVQlgTdcxN8vzTErvwYxjt8hJaY6sUKkLYEKtpWfn666/p2rUrpUqVSvaaXq/H1dXVbJmbmxuKoqAoSnaFKISwQprIi7gfb48m7jYG+yKEVduCwcXH0mEJIbKQVbSs7Ny5kwsXLjB48OAUX+/SpQs///wzf/75J1FRUZw+fZolS5bQoUMHnJ2dszlaIYS10Ib9TZ5/WqCJu43e2ZvQWr9LoSKEDbJ4y0pMTAzffvstH330ES4uLimuM3DgQPR6Pb179za1pLRo0YIJEya89PE1Gquo116ZpPxsPU/IPblKnom0IbtxOdENlSEavXsNImtsRK3LZx3fwDJAzqdtyS15ZvcsABYvVubOnUu+fPno2LFjquusWrWKZcuWMWrUKCpVqsS1a9eYMWMGY8eOZdKkSS91fDc3x5faPqfILXlC7sk1V+d5fQ0c6wmKHgq2QFt/I3nsUv6yk1Pk6vNpg3JLntnFosXK7du3WbJkCbNnzyYyMhLANGA2OjqaqKgo4uPjmTRpEiNGjKBnz54A1KxZEw8PDwYPHkzPnj2pVCnzj3cPD4/BYDC+fDJWSqNR4+bmaPN5Qu7JNbfnaX99Dk7nRwIQX6gTUVUWQKQKiLJQpC8nt59PW5Nb8nR3d0Stzr7WI4sWK7du3SIhIYH+/fsne61nz574+fkxduxY4uPjqVChgtnrST/fuHHjpYoVg8GIXm+7f1BJckuekHtyzXV5KgpOwV/hdO07AGKK9SfSZzIY1WDM+b+HXHc+bZyt55nd97ZYtFipUKECK1asMFt2/vx5vvnmG8aPH0/lypUpUKAAAGfPnqVmzZqm9c6cOQNAkSJFsi9gIYRlKAZczn+E4+2lAESVGUt0qREyfb4QuYRFixU3Nzdq166d4muVKlUytZi0bNmSGTNmoNfr8fX15erVq/zwww/4+/vj6+ubnSELIbKbIRa3f3tj/+AXFFREVphGbNHelo5KCJGNLD7ANj0mTpzI3LlzWbduHTNnzsTT05MWLVowfPjwbO0zE0Jks4RwXP7piN3j/SgqHeGVFxFfoL2loxJCZDOVkstnVXvyJMqm+xW1WjV58zrbfJ6Qe3LNLXnaGR6S50QneHIco8aF8KprSPBoaOmwslxuOZ+Sp23x8HDO1tuzc0TLihAilzFE4Xq4BURfwajzJMx/I3o3f0tHJYSwEClWhBBWRxNzA030FXAuQUT1Lejty1g6JCGEBUmxIoSwOgaXCoTV/wf3gj4YI1Vgw83pQogXk9GpQgirZHQpDzl8VlohRNaQYkUIIYQQVk2KFSGEEEJYNSlWhBBCCGHVpFgRQgghhFWTYkUIIYQQVk2KFSGEEEJYNSlWhBBCCGHVpFgRQgghhFWTYkUIIYQQVk2KFSGEEEJYNZWiKIqlg7Akg8H2nzmi0ahzRZ6Qe3KVPG2L5GlbckOearUKlUqVbcfL9cWKEEIIIaybdAMJIYQQwqpJsSKEEEIIqybFihBCCCGsmhQrQgghhLBqUqwIIYQQwqpJsSKEEEIIqybFihBCCCGsmhQrQgghhLBqUqwIIYQQwqpJsSKEEEIIqybFihBCCCGsmhQrQgghhLBqUqwIIYQQwqppLR3Aq3Do0CE2bdrEqVOnuHnzJu+88w7jxo1Lcd3Fixfz448/EhISgre3NyNHjqR27dovPEZISAhff/01+/fvR61W06RJE8aMGUOePHmyOJuM8fHxSfW1AwcOkD9//gxt6+npyaFDh7Iktqw2atQoNm/enGz5woULadCgQZrbJiQkMHPmTDZv3kxERARVqlTh008/pXz58q8q3EwxGAwsWbKEoKAgrly5gsFgwNvbmyFDhlCnTp0Xbm+t5/TatWt89dVXHDt2DEdHR9q0acPHH3+Mg4PDC7fdvHkz8+fP5/bt25QoUYLBgwfTunXrbIg6Y3bs2MHWrVs5e/YsYWFhFCtWjLfffpuuXbuiVqf+PbFHjx4cPXo02fLt27dTpkyZVxlypmzatInRo0cnW96vXz8+/vjjNLfNKecSUj8vAFOnTqVNmzYZ2s5azud///3H4sWLOXXqFJcvX6Z06dJs27Yt2XpBQUFMmzaN4OBgChYsyHvvvcc777zzwv1n1bXWJouV/fv3c/78eWrWrElYWFiq6y1evJhp06bx4YcfUrFiRdavX0+/fv1Yv359mh/6er2evn37kpCQwOTJk9Hr9Xz33XcMGjSIH3/8EZVK9SrSSpe1a9cmW/bJJ5/g6OiYZqGSpEePHrRt29b0s52dXZbGl9WKFSvGlClTzJal5wLwzTffsGXLFkaNGkWRIkVYtGgR7733Hlu3bsXLy+tVhZthsbGxzJ8/n/bt29OnTx+0Wi2bN2+mV69ezJ07l8aNG79wH9Z2TsPDw3n33XcpXLgwM2fO5PHjx3zzzTeEhoYmO5fP27lzJ6NGjaJ///7Uq1eP3bt38+GHH+Lq6kpgYGA2ZZA+S5cupXDhwowcOZJ8+fJx5MgRvv76a27evMknn3yS5rbVqlVLtk7RokVfZbgvbdGiRbi6upp+LlCgQJrr56RzCfD5558TGRlptmz58uXs2rXrhV8crPl8Xr58maCgIPz8/DAajSiKkmydEydOMGjQIN544w1GjRrF8ePH+eqrr9DpdHTu3DnN/WfZtVaxQQaDwfT/xo0bK+PHj0+2TlxcnFK9enVl0qRJpmV6vV5p3bq18sEHH6S5/19//VXx9vZWLl26ZFp27NgxxdvbWwkKCsqCDLLOzZs3FW9vb2XhwoUvXNfb21tZtGhRNkSVNT755BOlTZs2Gd7u3r17SoUKFZRVq1aZlkVERCi1atVSvvvuu6wM8aXp9XolNDTUbJnRaFQ6dOigdO/e/YXbW+M5nT9/vuLn56c8evTItOyXX35RvL29lStXrqS5batWrZRhw4aZLevdu7fSuXPnVxLry3g2vyQTJ05UKleurMTFxaW6Xffu3ZX+/fu/ytCy1MaNGxVvb+8U801LTjqXqWnSpInSr1+/NNex9vP57OdlatfUPn36KJ06dTJbNnbsWKVevXpm2z8vK6+1NjlmJa0m1iTHjx8nIiLC7BunRqPhtddeIygoKMXqMklQUBA+Pj6UK1fOtKxatWoUKVKEoKCglws+i23btg2VSmWWZ2538OBBDAaDWbOti4sLTZo0sbrzp9FocHd3N1umUqkoX748Dx48sFBUL2f//v3UqVMHDw8P07KWLVui0+nS/P3fvHmTq1evJvtbbtu2Lf/++y+PHz9+ZTFnxrP5JalQoQJxcXGEhoZmf0BWJKedy5QcP36cW7du0a5dO0uH8lJe9HkZHx/P4cOHk3VztWvXjpCQEM6dO5fqtll5rbXJYiU9goODAShdurTZ8jJlyhAVFcX9+/fT3DalroayZcua9mstfv31V2rWrEnBggXTtf6CBQuoVKkSNWrU4IMPPuDOnTuvOMKXc+PGDWrUqIGvry8dO3Zk9+7dL9wmODgYT0/PZOOLypQpw7Vr1zAaja8o2qxhNBo5ceJEuvu7re2cpvT+0el0FC9ePM33z9WrV4GU37OKophet2bHjh0jT5485MuXL831jh49StWqValcuTLdu3fn77//zqYIM69t27ZUqFCBpk2bMn/+fAwGQ6rr2sK53LZtG46OjjRt2vSF6+bE85nkxo0bJCQkJDtXZcuWBUjzPZuV11qbHLOSHuHh4eh0umQD+pK+xYaGhqb6AR8eHm7WN5vEzc3NqoqVCxcucOnSJSZMmJCu9du3b0+jRo3w9PTk0qVLzJ07l27duvHzzz8n+3ZvDSpUqEDlypUpW7YsERERrFmzhsGDBzNjxgxatWqV6napnT93d3cSEhKIjo7GxcXlVYb+UlauXMm1a9fSdV6t8ZyGh4fj5uaWbLmbm1uaY8ySXnt+26Q80trWGpw+fZpNmzYxePBgNBpNquvVrFmTN954g5IlS/LgwQMWL15Mr169WLlyJf7+/tkYcfp4eXkxdOhQ/Pz8UKlU7N27l+nTp3P//v1Ub2zI6edSr9ezc+dOmjZtipOTU5rr5rTz+bzUzlXSz2mdq6y81uaIYiUiIiJdTd7FihVDp9Ole78pDYRN6v550SDZ1LbN6sG1L5P71q1bsbOzo2XLluk61qRJk0z/r1mzJtWrV6djx46sW7eOfv36ZSzwTMhoru+++67Z8iZNmtC1a1dmzpyZZrECaZ/7V+1lzunRo0f57rvv6N27NzVr1nzhPix9TjMive+f59dJ73vWkkJCQhg2bBiVK1d+4e992LBhZj83atSItm3bMmfOHBYuXPgqw8yU+vXrU79+fdPPgYGB2Nvbs3z5cgYOHJjmwP6ceC4h8Y7TR48epat7Paedz9Skdk4y+1mZUTmiWPn9999TvDXueVu2bKFChQrp2qebmxtxcXHExcVhb29vWh4eHg6Q5rdONzc303rPioiISPEb48vIbO6KorB9+3bq16+f6dupy5cvT6lSpTh79mymts+olz3ParWaFi1a8N133xEbG5vqbbCpnb/w8HDs7Oxe+E3pZWU2zwsXLjBo0CCaNWvGiBEjMnXs7D6nKUnr/ZNW19az37o9PT1Ny5P2ldXvvawSERFBv379cHBwYO7cuRm+G8vJyYmGDRvy22+/vaIIs17r1q1ZsmQJ58+fT7FYyannMsm2bdvIkydPpu5aymnnM7XWrvScq6y81uaIYqVjx4507NgxS/eZdFEMDg6mYsWKpuXBwcE4OzunedtdmTJlOH/+fLLlV65cSdetpBmR2dyPHTvGnTt3Mv2hliS7Whsga85zeuItU6YMjx49IjQ01KyQCw4OplSpUukaoP0yMpPnjRs36Nu3LxUrVmTy5Mkv9c0zO89pSsqUKZOsuzQ+Pp4bN27w5ptvprpdUp/51atXzYqa4OBgVCpVsj51axAXF8f777/Pw4cPWbt2LXnz5s3Ufix9zrJaTjyXSWJjY9mzZw/t2rXL9DQAOel8Fi9eHDs7O65evWo2f9WVK1eAtKeKyMprba4dYFutWjVcXV3Zvn27aZnBYGDHjh00bNgwzQ+Dhg0bcunSJbML7smTJ7l9+zYNGzZ8pXGn19atW3Fycnqp4un8+fNcv36dypUrZ2Fkr47RaOS3336jXLlyaU4uFhgYiFqtZseOHaZlUVFR7N2712rO37NCQkLo3bs3np6ezJkzJ0Ndnc+zhnPaoEEDDh8+zJMnT0zLfv/9d+Lj49P8/RcrVozSpUubvWch8VtulSpVUrz7xpL0ej3Dhw/nwoULLFq0iCJFimRqP9HR0QQFBeWY9yEkTnim0WjMvgg+K6edy2ft3buXqKioTN8FlNPOp06nIyAgwOx6CYnnysvLK9VzDFl7rc0RLSsZdfv2bU6fPg1ATEwMN27cYOfOnQCmsQw6nY7333+fadOm4eHhYZoU7ubNm0ydOtVsX82bN2fQoEEMGTIEgBYtWuDj48OwYcP46KOPMBgMTJ48merVq5v13VqKXq/nt99+o1mzZjg6Oqa4TvPmzSlcuDDLly8HEifIu3nzJrVq1cLDw4PLly8zb948ChYs+MJJfyzh9u3bjBo1irZt21K8eHHCwsJYs2YNZ86c4YcffjBb9/lcCxQoQNeuXZkyZQparZbChQuzZMkSgGTjYCwtNjaWvn378ujRI0aNGmX6NpOkatWqpv/nlHPatWtXVq1axaBBgxg0aBCPHj3i22+/pV27dmbf0saMGcOWLVvMbo0cNmwYH374IcWLF6du3brs2bOHQ4cOsWjRIkukkqYJEybwxx9/MGLECGJjYzl58qTptbJly+Li4pIsx3/++YfFixebzuWDBw9YunQpISEhzJgxw0KZpK1Pnz4EBATg7e0NwJ49e1i3bh09e/Y0TfqV08/ls7Zu3UrhwoWpXr16stdy4vmMiYkx3UZ8+/ZtIiMjTZ+XSdeOwYMH0717d8aOHUu7du04fvw469evZ8KECWatI6/yWmuTxcqRI0fMxgQcOHCAAwcOAHDx4kXT8t69e6MoCitXruThw4d4e3uzYMECs9lrFUXBYDCYNdtptVoWLlzI119/zYgRI1CpVKbp9q1hYNjBgwd58uRJmoO/DAaD2W1jpUqVYteuXWzfvp2oqCjy5s1Lw4YN+eCDD6yy/9jZ2RkXFxdmz57N48ePsbOzw9fXl4ULFyYrGJ/PFRKn6ndycmL69OlERETg5+fH8uXLrWr2WoCHDx9y4cIFAAYPHpzs9Wf/nnPKOXVzc2P58uV89dVXDB06FAcHB9q2bZtsanaj0Zjs9tfWrVsTGxvLvHnzWLx4MSVKlGDatGlWOePpwYMHAfjuu++SvbZixQpq166dLEcvLy/i4+OZOnUqoaGhODo64u/vz/jx46lSpUq2xZ4RpUqVYsOGDdy7dw+j0UjJkiUZM2YMPXr0MK2T089lkrCwMA4cOMC7776b4rU+J57PR48eMXz4cLNlST8n/Z36+/szZ84cpk6dypYtWyhYsCBjx45N9qXnVV5rVUpO6jwTQgghRK6Ta8esCCGEECJnkGJFCCGEEFZNihUhhBBCWDUpVoQQQghh1aRYEUIIIYRVk2JFCCGEEFZNihUhhBBCWDUpVoQQQghh1WxyBlshssqzsxmnZcWKFWzevJmjR4+yd+/eVxxV1tm0aROjR49mz549FC1a1NLhJPPDDz8wa9Ys089arRYvLy8CAwMZPnx4irNgRkdHs2rVKrZv385///2HoiiUKFGC1q1b07Nnz2RPek1af9u2bdy6dQtFUciXLx+VKlXinXfeoVatWmbr37x5k6VLl3Lo0CHu3r2LSqWiaNGiNGvWjG7duqX5ENQks2bNYvv27Wzbti3LHpyZNGPsypUrM7zt9OnTCQoKYuPGja/8QZ5CZIbMYCtEGp59ngvAnDlzOHLkiOnZF0nKli3L48ePiYyMTPPBXtbm8ePH3Pi/9u49pur6f+D4k8AsFOImE0sjVI7IOUB4nIoeRSikxAmJWgi5I6aG2rTppIv3QVBBqYlJKKLLiYohrDlmDBNCEW9Ja07TkMRLJhyRTop2zu8PxmcegSPaV2H7vR4bG7zP+/L6nPPZzov35/35vGtqGDx48H/aIPFxaUlWsrKycHBwwGg0UlZWRnZ2Nl5eXuTn51vsfPvXX3+h1+upqakhLi6OESNGAHD48GG2bt1Kv379yM7Oxs3NDWh+PPi0adM4c+YM8fHxyiPQL1y4QElJCUOGDCEhIUHpv6SkhPfffx9nZ2emTZumfNZnzpxRvujz8/OtHtPVq1cJDw/nk08+UfYq+19o2TdqwIABD9325s2bhISEkJiYaHXnayE6jVkI0WFLliwxBwQEdHYY/2+sXbvW7O3tbb5+/bpFeWJiotnb29t86NAhi/IZM2aYBw8ebK6srGzVV2VlpXnw4MHmGTNmKGWHDh0ye3t7m3fv3t3m+P/++6/ye01NjTkgIMAcGRlpbmhoaFXXZDKZi4qKHnhMn376qVmn01n03RWsWrXKHBYWZjaZTJ0dihCtyHyfEP8jiYmJhISEWJSpVCpWrVpFXl4e48aNw8/PjzfeeIOTJ09iNpvJysoiJCSEl19+mbfffpsLFy606re8vJzp06cTGBiIv78/b775JocOHXpgPCaTiYyMDGVcrVbLhAkTLGaF9uzZg0ql4uLFi0pZXFwcERERnDp1ipiYGPz9/QkNDSUzM7PVJmUNDQ2kpKQQGhqKWq1mxIgRvPPOO5w7d06p09TUREZGBuHh4ajVaoYPH84HH3xAXV1dh9/b+6nVaqB5E7YWVVVVlJWVMWnSJLRabas2Wq2WSZMmUVZWxi+//AKAwWAAaHdTtXsviWzZsgWj0cjy5ctxcHBoVdfGxoawsDCrcTc1NbF7924iIiIs+r548SIqlYqsrCwyMzMJCQnBz8+PuLg4fv/9d+7cucPnn3/OqFGjGDJkCHPnzrU4dmj+3O7dPLClz02bNpGdna2cZ1OnTm01YwgwceJEqqurOXz4sNVjEKIzyJoVIR6zAwcO8Ouvv7Jo0SJsbGz47LPPmD17NpGRkfzxxx8sW7aMmzdvkpKSwvz589m7d6+yo+vevXtZsmQJoaGhpKamYmdnR25uLvHx8WzatEm5zNGWrKwsvvrqK9599120Wi13797l/Pnz3Lx584ExX7t2jcWLF6PX65k3bx779+8nLS0Nd3d3IiMjAWhsbCQmJoba2lpmzpyJv78/RqORyspKrl27Rv/+/TGZTCQkJHDs2DHi4+MJDAyktraWdevWcerUKfLy8njmmWce+j1tSa48PT2VsvLycgBeeeWVdtuFhoaSm5tLeXk5arUatVpNt27dSEpKwmAwMHz4cNzd3dtsW1ZWhpubGwEBAQ8db4tTp05hMBgYNmxYm69v374db29vli1bRkNDA6mpqcyZMwd/f3/s7OxITk7m0qVLpKam8tFHH/H1118/cMxvv/0WLy8vPvzwQwDWrFnDrFmzKC4utki6fH19sbe358cff7R6XgnRGSRZEeIxa2pqYvPmzRYLO+fOnUtFRQXfffedkpjU1dWRnJzMmTNnUKlU/PPPPyQnJxMcHMz69euVtmPGjCEqKor09HR27drV7rjHjx/H29ub+fPnK2U6na5DMRsMBr755htlDUdQUBBHjhyhsLBQSVZycnI4e/Ys2dnZBAUFKW3vnV3Yt28fpaWlrFu3zqJ80KBBREdHs2fPHmJiYh4Yj8lk4u7duxiNRn766Sd27NhBREQEvr6+Sp1Lly4BWF0o3PLavXVXrFhBUlISixcvBppnWUaOHMnkyZMtZmguX76Mj4/PA2O15sSJEwAWcd/LwcGBjIwMZdalvr6e5ORkvLy82LBhg1Lv/Pnz5OTk0NjYSM+ePa2O2aNHDzZu3IitrS0A7u7uTJ48mYMHDzJ+/Hilnq2tLYMGDeL48eP/6RiFeBzkMpAQj9mwYcMsEpX+/fsDMHr0aCVRube85Yv0xIkTGAwGoqKiuHv3rvJjMpnQ6XRUVVVhNBrbHVej0XD69GlWrFhBaWkpjY2NHY65V69eSqLSQqVSKbEBlJaW4unpaZGo3K+kpARHR0fGjh1rcQw+Pj706tWLI0eOdCiekSNH4uvry9ChQ1mwYAG+vr6kpKR0+Hjud+/7Hh0dzcGDB0lLSyMuLg4PDw8KCgqIjY0lKyvrkcdoy59//omNjQ3Ozs5tvj5mzBiLy0Mt50RwcLBFvfvPFWuCg4OVRAWaE0WA2traVnVdXV25evXqA/sU4kmTmRUhHrPnnnvO4u+Wu1faK799+zbQfGcLwHvvvddu3zdu3Gh1K26L2bNnY29vT0FBATt27MDW1hatVsuiRYvQaDRWY3ZycmpV9vTTTyuxQfNMkIeHh9V+rl+/TkNDg7LG5H719fVW27fYsmULPXv25MaNG+zcuZOioiJWr17NqlWrlDp9+vQBmi8ReXl5tdlPy+Wj++N2cHAgIiKCiIgIAM6ePYter+fLL79kypQpODo64uHhYbG251Hcvn0bOzs7i+ThXo96rlhz/2fZctdXW227d+/OrVu3HtinEE+aJCtCdFEt/30vXboUf3//Nuu4urq2297Ozg69Xo9er6ehoYHy8nK++OILZs6cyYEDB3j22Wf/U3wuLi5cuXLFah1nZ2ecnJzanaHo0aNHh8ZSqVS4uLgAzbMs8fHx7Ny5k+joaItLVenp6fzwww+MHj26zX6Ki4uVutYMHDiQ119/nZycHKqrq/Hz80On07Ft2zZOnjz5yOtWnJ2duXPnDkajsd0kszMZDIZ2Z32E6ExyGUiILiowMBBHR0d+++03NBpNmz8dfTaKo6Mj4eHhxMTEYDAY2rwE8LB0Oh3V1dVW70wKDg7GYDBgMpnajL+9GRBrbGxsWLZsGba2tqxZs0Yp12g0jBo1iry8PI4dO9aq3dGjR8nLy0On0ykzPfX19TQ1NbU5zvnz5wGUBbfTp0/H3t6elStXtrlI2Ww2s3//fquxv/TSSwDU1NR04EifvIsXLz7Sc1qEeNxkZkWILqpHjx58/PHHJCYmcuPGDcaNG4erqyt1dXWcPn2auro6Vq5c2W77OXPmMHDgQNRqNS4uLtTW1pKTk8Pzzz/Piy+++J/jmz59Ovv27SMhIYFZs2bh5+fHrVu3qKysJDg4mOHDhzN+/HgKCwuZNWsWcXFx+Pn50a1bN65cuUJFRQWhoaG8+uqrDz22p6cnU6ZMYfv27Rw9elRZCJuamoperyc+Pr7Nh8J5eXlZrHWpqKggKSmJCRMmEBgYiJOTE9evX+f777+ntLSUyMhIevfuDUDfvn1JT09n4cKFTJw4kdjYWGXB7blz58jLy8NsNls9npa7gH7++Wdl7UhXUV9fT3V1NbGxsZ0dihCtSLIiRBc2ceJE+vTpQ1ZWFsuXL+fvv//GxcUFHx8foqKirLYdNmwYRUVF7Nq1i8bGRnr16kVQUBAJCQkWT319VD179mT79u2sW7eOnTt3sn79ehwdHdFoNEyZMgVovsNkw4YNbN26lb1795KZmYmtrS29e/dm6NCheHt7P/L48+bNIz8/n7Vr17J161YA3NzcyM3NZdu2bezbt0959Hy/fv2YPXu2MjvSIiAggEmTJlFRUUFBQQH19fV0796dAQMGsHTpUt566y2LMceOHUthYSGbN29mx44dXL58maeeeooXXngBnU73wC96Dw8PtFotxcXFTJ069ZGP/XEoLi6mW7duvPbaa50dihCtyOP2hRDiCSoqKmLhwoWUlJR0aB+hJyUmJgYPDw/S0tI6OxQhWpE1K0II8QSFhYWh0WjYuHFjZ4eiqKyspKqqigULFnR2KEK0SZIVIYR4gmxsbFi9ejXu7u6tti/oLAaDgdTUVPr27dvZoQjRJrkMJIQQQoguTWZWhBBCCNGlSbIihBBCiC5NkhUhhBBCdGmSrAghhBCiS5NkRQghhBBdmiQrQgghhOjSJFkRQgghRJcmyYoQQgghurT/A2pYGV53p69FAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get df for cases at t=0 and after (Post-ROSC)\n",
    "df2_rosc = df2_sns[df2_sns['time'] >= 0]\n",
    "\n",
    "# Survived------------------------------------\n",
    "df2_survived = df2_rosc[df2_rosc['ROSC-Survived-CPC-1or2'] == 'yes'].copy()\n",
    "md_survived = smf.mixedlm(\"b_ox ~ time\", df2_survived, groups=df2_survived[\"patient\"])\n",
    "md_survived_f = md_survived.fit()\n",
    "# print(md_survived_f.summary())\n",
    "\n",
    "surv_intercept_rosc = round(md_survived_f.params['Intercept'], 2)\n",
    "surv_time_coef_rosc = round(md_survived_f.params['time'], 2)\n",
    "\n",
    "#Died----------------------------------------\n",
    "df2_died = df2_rosc[df2_rosc['ROSC-Survived-CPC-1or2'] == 'no'].copy()\n",
    "md_died = smf.mixedlm(\"b_ox ~ time\", df2_died, groups=df2_died[\"patient\"])\n",
    "md_died_f = md_died.fit()\n",
    "# print(md_died_f.summary())\n",
    "\n",
    "died_intercept_rosc = round(md_died_f.params['Intercept'], 2)\n",
    "died_time_coef_rosc = round(md_died_f.params['time'], 2)\n",
    "\n",
    "# Plot it -------------------------------------\n",
    "# Define the slope and intercept for the two functions\n",
    "m1rosc, B1rosc = surv_time_coef_rosc, surv_intercept_rosc  # Function 1: y = m1 * t + B1\n",
    "m2rosc, B2rosc = died_time_coef_rosc, died_intercept_rosc  # Function 2: y = m2 * t + B2\n",
    "\n",
    "# Generate values for t\n",
    "trosc = np.linspace(0, 10, 100)\n",
    "\n",
    "# Calculate the corresponding y values for both functions\n",
    "y1rosc = m1rosc * trosc + B1rosc\n",
    "y2rosc = m2rosc * trosc + B2rosc\n",
    "\n",
    "# Set up the plot\n",
    "sns.set_theme(rc={'figure.figsize':(6,4)})\n",
    "sns.lineplot(x=trosc, y=y1rosc, label=f'Survived: y = {m1rosc}*t + {B1rosc}', color='blue')\n",
    "sns.lineplot(x=trosc, y=y2rosc, label=f'Died: y = {m2rosc}*t + {B2rosc}', color='orange')\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Mixed Effects Regression from [0, 10] min')\n",
    "plt.xlabel('Time since ROSC (min)')\n",
    "plt.ylabel('rSO2 (%)')\n",
    "plt.xlim(-10,10)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7524d68b-fbc2-47a1-8e50-0b138257a19d",
   "metadata": {},
   "source": [
    "### Graph both pre- and post-ROSC fits on the same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b3254a53-e785-4254-a6fa-40e05688f1f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/statsmodels/regression/mixed_linear_model.py:2201: ConvergenceWarning: Retrying MixedLM optimization with lbfgs\n",
      "  warnings.warn(\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/statsmodels/regression/mixed_linear_model.py:2201: ConvergenceWarning: Retrying MixedLM optimization with cg\n",
      "  warnings.warn(\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/statsmodels/regression/mixed_linear_model.py:2207: ConvergenceWarning: MixedLM optimization failed, trying a different optimizer may help.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/statsmodels/regression/mixed_linear_model.py:2219: ConvergenceWarning: Gradient optimization failed, |grad| = 0.074612\n",
      "  warnings.warn(msg, ConvergenceWarning)\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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bvXo127FjR+59euDAgeySJUvY69evc23y8vLYyMhI9sUXX7S57+T+QakUpEl1794dISEh2Lt3L5KTk3H58uVq0yiq89BDD+GFF17Apk2bsHTpUowePRoTJkzg9v/+++/g8XgYPXo09Ho99+Xl5YXo6GicPXsWgDGHTKPRYNSoUWbn79KlCwIDA+3qz549eyy+xo8fb9fzKnf+/HnI5XI8+uijZv1nGAZ9+vTB5cuXoVQqoVKp8Pfff2P48OHcKJ89QkND4erqio8++gjbt2/HrVu37DpeqVSiY8eO6NixI3r27ImlS5dixIgRePHFF7k2J06cgF6vx5gxY8yei4ODA7p168b9LFJTU5GXl4fhw4ebPUZAQAA6d+5s9fEHDhwIkUjEfZ+Wlobbt29zP8/Kj9e3b1/k5eUhNTUVABAbG4vjx4/jo48+wpkzZ6BWq83O7ebmhpCQEHz99df45ptvcO3aNbP8w+qcPn0aUqmUG4ErVz5SXTUVpvLdAwDw8vKCp6cnMjMza32scjKZDMOGDcPevXuh1+uxf/9+jBs3zuoobfmt7EcffdRse1xcHNq0acP1z57fjd9//x3t2rVD+/btza557969wePxuJ+xNbGxsUhKSsLSpUvx559/2j1RcOTIkWbPMzAwEJ07d7Z6y37o0KEW/XZxccGAAQPM+t2+fXt4e3tz/WYYxmy/wWCosU96vR5ffPEFRowYgZiYGHTo0AExMTG4c+cOUlJSLNpXThMCwK0iUp4u1lDvUwkJCdydMsB4R2vEiBFIS0vj0mhOnz6NhIQEi1HSRx99FCqVCufPnwdQv59bbdfTlvcVW7EsCwBmr5EFCxbgjz/+wPLlyzFp0iRIpVLs2LEDjz32mFkqFXlwUSoFaVI8Hg/jxo3Dli1boNFoEBYWhq5du9p9ntGjR+PTTz+FVqvFrFmzzPYVFBSAZVkkJiZaPbb8dlz5bfPKfyzKWdtWHWdnZ8TGxtrcvjb5+fkAgIULF1bbpri4GHw+HwaDAb6+vnV6HGdnZ2zZsgVffPEFVq5cieLiYnh7e2PixIl45plnzIJOaxwdHfHtt99yfd6wYQMOHjyIqKgozJkzx+y5VPchoTynr6ioCADg6elp0cbLy8tqoFg1T7D8sT744AN88MEHVh+v/HHeeust+Pn54dChQ/jqq6/g4OCA3r1747XXXkNYWBh4PB42btyINWvWYP369fjvf/8LNzc3jBo1Ci+88IJZMFuZXC6Hl5eXRVDq6ekJoVBosbyTm5ubxTnEYjE0Go3V81dn/PjxeOKJJ/DFF1+gsLCQC8St9Q8AfHx8LPb5+PhwwZg9vxsFBQVIS0vj0nSqKr/m1sydOxdSqRQ//PADduzYAYFAgK5du+KVV16x6Xequv5VXXZLIpFY/MwKCgpQUlLC5a9W1+81a9Zg9erV3PbAwED89ttv1fbpv//9L7Zu3YrZs2ejW7ducHV1BY/Hw1tvvWX151r1NVCez1r+Ya2h3qdqOl4ul8PPzw9yudxq/m3566W8L/X5udV2PW15XwGMH5oB41yANm3aWH2s8veNqoG+l5cXHnvsMW5Q5u+//8bs2bPx/vvvY+TIkXB3d4dEImnQJS5J60GBMWly48aNw2effYYdO3bUaRTAYDDglVdegaurK8RiMd58801s376d+4Pi7u4OHo+HrVu3VjtpAqj4g1QeUFWWn59v12hMQyrPefvPf/6D+Ph4q208PT3BMAwEAgFyc3Pr/FhRUVFYuXIlWJZFcnIy9u3bhzVr1sDR0dHsj5A1fD7f7I9gr169MG7cOKxevRqjRo2Cv78/91w+++wz7g+ZNeXtqk7aA6z/fABYBJ/l55g7dy6GDBli9ZjyfEypVIqFCxdi4cKFyM/Px/Hjx/G///0P8+bNw5EjRwAY/2AvX74cgHFE+/Dhw1i9ejW0Wi03y70qNzc3XLx4ESzLmvWvoKAAer3eaj5jQ3jooYcQHh6ONWvWIDExsdq8yPLX/L179yyWQbx37x7XP3t+N9zd3eHg4MBdq6pqes5CoRAzZszAjBkzUFJSgpMnT2LlypV4+umn8ccff9S6ckR1/asabFobPXd3d4ebmxvWr19v9dzlyyZOnDgR/fv357bXtvLJDz/8gLFjx+Kll14y215UVGTXsmXlGup9qrrjKz+Gm5sbN1Gtsnv37gGo+FnW5+dW2/W05X0FABITE7Fz5078+uuv1QbGv/76K4RCodlKQNZ069YNvXr1wi+//IKCggJ4enqiZ8+e+PPPP5GTk9PilwwlDYtSKUiT8/X1xaxZszBgwACMHTvW7uM/++wz/PPPP/joo4+wcuVKJCcnczOgAaB///5gWRa5ubmIjY21+Cq/VdmpUyc4ODjgwIEDZuc/d+6cXbeyG1qXLl3g4uKCW7duWe1/bGwsxGIxHB0d0a1bNxw5cqTGWfRVR6Cs4fF4iI6OxhtvvAEXFxdcvXrV7n6LxWIsWbIEGo0Gn3/+OQCgd+/eEAqFSE9Pr/a5AMaA1dvbG4cPHzY7Z1ZWFnf7tjYREREICwtDUlJStY9lbaTXy8sL48aNwyOPPILU1FSoVCqLNuHh4Zg/fz4iIyOtrqZRLiEhAUqlEr/88ovZ9u+//57b31ieeeYZDBgwADNnzqy2TfkSVVWXcLt06RJSUlK4/fb8bvTv3x8ZGRlwc3Ozes1tXTvbxcUFw4YNwxNPPAG5XG7T7+DBgwe52+WAcYTw/PnztQZC5f2Wy+VgGMZqv8snyfn6+lp9/6gOj8ezuNvyxx9/1PkDbEO9T506dcosODYYDDh06BBCQkK4wC8hIQGnT5+26Ov+/fshkUisLktZ3c+t/H2n6ii5vdfT2vsKAAwZMgRt27bFunXruBSpyg4dOoQTJ05g/Pjx3Ch4fn6+1ZQog8GAtLQ0SCQSbiWXuXPngmVZvPXWWxYrygDGCZ013TkgrReNGJNm8corr9TpuL/++gvr1q3D/PnzuSDjpZdewgcffIAePXpgyJAheOihhzBp0iS88cYbuHLlCrp16waJRIK8vDz8+++/iIyMxBNPPAFXV1fMnDkTn3/+Od58800MGzYMOTk5WLVqVbXL+VhTUlKCCxcuWGwXi8Xo0KGD3c9RJpPhrbfewuLFi1FcXIyhQ4fC09MThYWFSEpKQmFhIZYtWwbAWN3s8ccfx8SJEzFnzhyEhISgoKAAv/32G5YtWwYnJye0a9cOgHEpNJlMBgcHBwQFBeHChQvYtm0bBg8ejODgYLAsi6NHj6KkpMRsTVx7dO/eHf369cO+ffswe/ZsBAcHY+HChfjkk0+QkZGBvn37wsXFBfn5+bh8+TIkEgkWLlwIPp+P5557DkuWLMHChQvx2GOPoaSkhJvRXt2qBlUtW7YMs2fPxqxZs/Doo4/C19cXxcXFSElJwdWrV/HZZ58BMM6a79+/P6KiouDq6oqUlBTs378fnTt3hkQiQVJSEt59910MGzYMoaGhEIlEOH36NJKTk2scSR87diy2bt2KRYsWITMzE5GRkfj333/x5Zdfol+/ftWm9zSEMWPGYMyYMTW2iYiIwKRJk/Dtt9+Cz+ejb9++3KoU/v7+3DJm9vxuPPXUUzh69CimTJmC6dOnIyoqCgzDIDs7GydOnMDMmTOrvfMxb948tGvXDjExMfDw8EBmZiY2bdqEwMBAhIaG1vqcCwsLsWDBAkycOBGlpaVYtWoVxGIx5s6dW+uxjzzyCA4cOIA5c+Zg6tSpiIuLg0gkQk5ODs6cOYNBgwZVe+ehJv379+dWn4iKisLVq1fx9ddf13nUsaHep9zd3fHUU09h/vz53KoUt2/fxsqVK7k2CxYswO+//45p06ZhwYIFcHV1xYEDB/DHH3/g1Vdf5YJGW35ukZGRAIBNmzbh0UcfhVAoRHh4eLVpSDWx9r4iEAjw2WefYebMmZg8eTJmzJiBTp06QavV4vfff8euXbvQvXt3syUw9+/fj507d2LkyJGIjY2Fs7MzcnJysHv3bty8eRMLFizgAvrOnTtj6dKlWLZsGR577DFMnjwZ7dq1g16vx7Vr17Br1y60a9fOIkectH4UGJNW4969e3j11VfRvXt3LFiwgNs+Y8YMnD17Fm+88Qbat2+PoKAgvPPOO4iPj8fOnTuxfft2MAwDHx8fdOnSxayQxvPPPw+pVIpt27Zh//79iIiIwLJly+yq3Hbu3DlMmjTJYruvry+OHz9ep+c6ZswYBAQEYP369Xj77behUCjg4eGB9u3bm02cio6Oxp49e/DZZ5/hf//7HxQKBby9vdGzZ0/uDT44OBhvvPEGNm/ejGnTpsFgMGDFihXo1KkTXFxcsH79ety7dw8ikQjh4eH473//azE5yx6vvPIKxowZg7Vr12LFihWYO3cu2rRpg82bN+PHH3+EVquFt7c3YmJi8Pjjj3PHTZo0CTweD+vXr8eCBQsQGBiIOXPm4Ndff0V2drZNj92zZ0/s3r0bX3zxBZYvX46SkhK4ubmhTZs2ZhP7evbsid9++w2bNm2CSqWCr68vxo4di3nz5gEw5i+HhIRg27Zt3MSk4OBgLFq0qMYSvA4ODti8eTNWrlyJ9evXo6ioCL6+vpg5cyaeffbZulzOBrd06VIEBwdjz5492LZtG5ycnNCnTx+8/PLLZmkPtv5uSKVSbN26FevWrcPOnTtx9+5dODo6wt/fH4mJiTXe6u/Rowd++ukn7N69G2VlZfD29kZiYiLmz59fa447YFwX/PLly3j99ddRVlaGuLg4fPzxxwgJCan1WIFAgM8//xybN2/G/v37sW7dOggEAvj5+aFbt25cYGevN998E0KhEOvWrYNSqUSHDh2watUqfPrpp3U6H9Aw71MDBw5E27Zt8cknnyA7OxvBwcH46KOPMGLECK5NREQEduzYgY8//hjvvPMO1Go12rRpgxUrVpjlrdvyc+vRowfmzp2L7777Drt37wbDMNi8ebNF5VNbVX1fAYA2bdrg+++/x4YNG7B//36sXbsWAoEAbdu2xRtvvIGJEyeavY769++P/Px8HDt2DNu3b0dJSQlkMhmioqLw4YcfWnywLC+8tHHjRqxfvx55eXkQiUQICwvDyJEjMWXKlDo9F9Ky8djK96EIIaQFKSkpwdChQzF48GC8++67zd0d0kKcOXMG06ZNw6effmqxAgixFBUVhSeffBJLlixp7q4Q0uLRiDEhpEXIy8vDF198gR49esDNzQ1ZWVnYuHEjFAoFpk2b1tzdI4QQ8gCgwJgQ0iKIxWJkZmZi2bJlKC4uhqOjI+Lj47Fs2TIuT5oQQghpTJRKQQghhBBCCGi5NkIIIYQQQgBQYEwIIYQQQggACowJIYQQQggBQIExIYQQQgghAB7wVSlYlgXD0NxDe/H5PLpudqJrVjd03exH16xu6LrZj65Z3dB1sw+fz7O5+mlDeKADYx6Ph5ISJfR6y9rpxDqhkA93dxldNzvQNasbum72o2tWN3Td7EfXrG7outnPw0MGgaDpAmNKpSCEEEIIIQQUGBNCCCGEEAKAAmNCCCGEEEIAUGBMCCGEEEIIAAqMCSGEEEIIAUCBMSGEEEIIIQAoMCaEEEIIIQQABcaEEEIIIYQAoMCYEEIIIYQQABQYE0IIIYQQAoACY0IIIYQQQgBQYEwIIYQQQloQhtFCqbyM/Pyt0OsLm/SxhU36aIQQQgghhJiwLAudLgdK5SUolZehVF6CSn0dLKsDAISFjWjS/lBgTAghhBBCmgTDqKBSXYdCeYkLhvX6PIt2AoE7pNJYCASuTdo/CowJIYQQQkiDY1kWWm0GNxJsHA2+AcBQpaUQEkkkpJJYSKVxkErjIBYHgcfjQSCQNWmfKTAmhBBCCCH1ZjCUQam6UikQvgyDQW7RTij0hlQaB5kpCJZIosHnS5q+w1ZQYEwIIYQQQuzCsgw0mttmQbBakwKANWvH44khkbQ3jQTHQiqJg0jkCx6P1zwdrwUFxoQQQgghpEZ6vdwYBKsuQ6m8CKXyKhimzKKdWBRoDIBNgbCjYzT4fFEz9LhuKDAmhBBCCCEcltVDrb7F5QUrlJeg1aZbtOPxHCGVdoRUGl9pNNizGXrccCgwJoQQQgh5gOl0+cYgWFWeFnEVLKu2aOfgEGY2Qc7RsQ14vPsrlLy/ng0hhBBCCKkWw2ihVidzI8FK5SXodNkW7fh8J1NKhCkQlsRCKGzapdOaAwXGhBBCCCH3IfPiGcYJcpWLZ1TgwdGxLaSSGC4twsEhHDzeg1cgmQJjQgghhJD7QEXxjIum1SKqK57hVrFKhDQOUklHCAROzdDjlocCY0IIuQ+oDHr858YlXCgtgpQvgKtADFeRCK5CEVyFYtO/lf4vqvi/k0DYYpdOIoRYZyyekW62XJq9xTOIJQqMCSHkPvBzQQ6OF94DAJRAhxxYTpypjgA8Loh2qRJEu4mqCayFIgj5D95tVkKai8FQCqXqqikQvgil8ooNxTNiIZG0bzHFM1oDCowJIeQ+EOJY97KpBrAo1GlRqNMCUNh8nEwghFs1o9DVjU5L+AIaqSKkFpWLZ5RPkNNobsOyeIbIVDwjnssPbsnFM1oDCowJIeQ+0MnFHV/F9sQ5lRw5pQrIdVrIdVoU63WmLy30LFv7ieygMOihMOiRqVHZfIyIxzMLnt1EYst0jyrbnIUiCOgPPbmPGYtnXDIV0LhUbfEMkcgfUmk8Nxrs6BgFPl/cDD2+f1FgTAgh94mHXD0wOCwYRUUK6PWM2T6WZaFkDJWC5UpBc5UAuvz/JXotFIaq+Yr1o2NZ5Os0yNdpbD6GB8C5PJA2BcsuVoJobvTalBbiwBc0aN8JaQgMo4dSeR0lpRe4/GBrxTP4fAkkko6VSinHQiTyaoYeP1goMCaEkAcAj8eDTCCETCBEoB3H6RjGPIjWa1GsswyiKwfcJXodmNpPbTMWQIlehxK9DhlQ2nycI19QEUxXMxHRTSSCh4MDQsQsoNfDkeXTbWjSoCoXz1CpLkGpvAaGsbzL8iAUz2gN6IoTQgiplojPh5fYEV5iR5uPYVgWZQa91SBaXk1gXazTQsM2ZDgNqBkD1FoDcrV2TETk8bjAuepExPJ8aTehZfoHTUQkQNXiGcZJctaKZwgEzpBIYh644hmtAQXGhBBCGhSfx4OLKd0h2PZ4GmqDoVIAbX10uqRykK3TotSgb9C+G9immYjoJhTBkSYitmrG4hnZVZZLq6F4hjQOzk7x8PfvAY3GFw2cpUQaCAXGhBBCWgRHgQCOAgl8HWxfWkrPMijV682C5co50sZtOot0kJYwEVHM49ccRNNExBaFYVRQqq5VTJKrtniGu2m5NONosETSEQKBcdUYoZAPmUwGrVYBNGjCEWkoFBgTQghptYQ8PtxFYriLbJ+ZXz4RsXzSYSmrh17MR1ZxKQo1miabiKhlGeTpNMir40TEqkF01RU+3ERibpIiTUS0T9XiGQrlJajVN0HFM+5/FBgTQgh5oFSeiBgA4yieu7sMRTLL1Twq0zIMNwpddSKiXK+12Nb4ExFtVz4R0WoAbWVk2lUoguwBqohoMJRBqbpilhZRXfEMmTSeyw2WSKKpeMZ9hgJjQgghxAbiek5ELE/pKOECa5315fP0WmiYlj0R0cNBDP8SJ4h1LJx4wlY1EbFy8Yzy0WDrxTPEpuIZ5culxUEs9mueTpMmQ4ExIYQQ0kjqPBGRMZhGnrW1TkQsD7hb40TE8hU+GnMiorF4xmUoVeWllK0XzxCLAitWiZDGwtExGny+qFH6RFouCowJIYSQFsaRL4CjgwC+DrZH01UnIloE1pW2taqJiNUE1tYmIrKsHmr1LW4k2KbiGZIYSKVxVDyDAGhBgfHu3buxZcsWpKamwsnJCfHx8fjiiy+4/ceOHcPKlSuRkpICPz8/TJ8+HU8++WQz9pgQQghpORpiIqK8ytrSFhMRTduUTAuZiCgQwFlggBNUkKIIEuYeZCiFE08BJ54SMp4LnHgRcBe7wVsaDl9Ze7g5xVLxDFKtFvGqWLVqFTZu3Ih58+YhPj4excXF+PPPP7n958+fx/z58zFmzBgsXrwY586dw3vvvQexWIwJEyY0Y88JIYSQ1qvqRERbMXwWPJkIafnFppU8mqkiosGAEgMASExf1TwLNYAS438l/HS4CLMtVvCoWhHxQZ2I+KBr9sA4JSUFn3/+OdatW4fevXtz24cMGcL9f82aNejQoQOWL18OAOjZsyeys7Px6aef4rHHHgO/hSf6E0IIIfcTMV8Ad0cJRDIGegeZTcfUVBHR6kREnRrFeh00DZvpARVjgKqeExGr5lC7mK3y0XomIhJLzR4Y79u3D8HBwWZBcWVarRanT5/GK6+8YrZ91KhR2LVrF65du4aYmJim6CohhBBC6qimiYjmxTOMy6XpefmAGNCwIihYKcpYKVR8X+hEkdCIwqDmB0DB80CpARbBdkuaiMgtkycUw00shq+TFI4GHpz5QovRaRehCBKqiNismj0wvnjxIiIjI7FmzRp8++23KC0tRadOnfDmm2+iffv2SE9Ph06nQ0REhNlxbdu2BWAccabAmBBCCGkdjMUzMsyCYJX6BqornuEpjeMKaNhaPMPeiYjludWGRpqImFV5IqJlsTwzYh4fLkKRRTpHxQi15TYXoQj8+yiYNugZ5N0pRVaSHImj28LZ044lXeqp2QPjvLw8XL16FTdv3sSyZcsgEomwevVqzJgxA0ePHkVxcTEAwMXFxey48u/L99eVQEC3OexRfr3outmOrlnd0HWzH12zuqHrZj97rpnBUAqF4ioUyktQKIxf1opniEQ+kMniIJPGQSaLh1TaHnx+3QIiIfhwFAnhDduPZ1kWCoOem2QorzTZsHKaR/nyeOX/KhuhImK+ToN8Oycillc5LA+cubxpkdg4Wi2qWOWj/HtxC6qIqFHokJUsx93rcmTflEOvMWak9xzZpkn70eyBMcuyUCqVWLVqFdq1awcA6NixIwYNGoSdO3eiS5cuAFDtJ8T63m5wcaGKNXVB181+dM3qhq6b/eia1Q1dN/tVvWYsy0ChuInikgsoKb6A4pLzUChuoWrxDD5fDGfnGLi6dIaLa2e4usTD0dGe6X+NwwNAsJ3HaAzGVT2KtMYvuU5j/FerRZFOY/xXq0WRVsPtl2u1DT4RsTxXG2qlzcdJBQK4iR3gLjKmebiLHYz/isz/7yZ2gLtpv7Ow4SYiFuUokHopH3cu5SMnpRiVB+ylLmKExXlB6mL7KisNodkDY1dXV3h5eXFBMQD4+PggIiICt27dwoABAwBYjgyXlBinl1YdSbZXSYkKBkPDVhi6nwkEfLi4SOi62YGuWd3QdbMfXbO6oetmv/JrVlCQhdLSi8aRYOUlKBRXrBfPEAeajQZLJFFmxTNUKkClsj13t6URAfCBED4CISCQorqB6vLrJi9Wolir5Uai5TptxSg1ly+tRZFOZyxDbvpe3cAVEZUGA5QqJbJUtgfTQlOueNXR58rfGyckiuFu2u4iFEHE54MxsMhLK0VmUhEyr8lRWmA+AdLNT4rA9m4Iau8Oj0AZeHweBKKmTRFp9sC4TZs2yMrKstjOsiz4fD5CQkIgEolw+/Zt9O3bl9t/69Yt7vj6MBgY6PX0Rmgvum72o2tWN3Td7EfXrG7outWMZXVQmYpnqFWXoVJfgUp1x6KdWfEMU36wSORp1oZhAKaBg7zWhGVYSCGAVCRBgEhiXGnOBrZURGzsiYj6yhMRbajhItbxEJ4nQtQ9B4TeE8FBVxHosnzAECCEMMIRLm2c4OohgVAkhkrIQ5lOB0e+AA2c9l2rZg+M+/fvj++++w43btxAZGQkACA3Nxe3b9/GuHHjIBaL0bNnTxw+fBjTp0/njjt48CC8vb3RoUOHZuo5IYQQcv/S6fKMpZSVl0zllK+CZS2XOHNwCDMFwHGQSmPg6NiWimc0kvpURJSb1pI2r35YnkvdsBURXZR8tMkVo809EYILRBCwFcGwSsTgto8OKb5a3PHSQSsyPc4901clYh4fvw0chkCpbUsCNoRmf+UOGTIEHTt2xHPPPYfnn38eYrEYa9asgYeHByZOnAgAWLBgAaZMmYK33noLo0aNwrlz57B792688847tIYxIYQQUk8Mo4VKncStEqFUXoJOl23Rjs93glQaCyenePj5dgfDtAPg3PQdJjara0VEhcFgPgpdY0VEHSR5BgRkC9DmnhjepebhZYHMgBRfLVJ8tchy04O1MXTTsgwYtmnvLPBYtqkHqS0VFBRg+fLlOHbsGPR6Pbp164bXX3/dbIm2Y8eO4eOPP+ZKQs+YMaNBSkIXFSno1pkdhEI+3N1ldN3sQNesbui62Y+uWd08aNeNZVnodNkVo8HKy1Cpr4NldVVa8uHo2BZSaSy3XJqDQxh4PP4Dd80ayv103fRaA3JTSpCVLEd2shzqskopGzzAMdARgggHaINFKHZmzIPpSqkeVSsiuugV6FF2Az1Lk9GzNBnRcw6C7x7WZM+r2UeMAcDT0xP/+9//amzTr18/9OvXr4l6RAghhNwfzItnXDYWz9BbLqYrELhDKo2DTGoMgiWSjhAImu4WNmn5VCVaZCXLkZUkx73bJTDoK8ZWRQ4C+LVzQUC0O/zaucJBaluIWV4R0eHcWrimHIRTYRJ4aL4x2xYRGBNCCCGk/ozFM9K50WCF8hLU6puorniGtA7FM8iDg2VZyHNUyEqSIyu5CEWZ5qtXyNzE8I92Q2C0G7xCnSEQ2pAjYdACgoq0jvKKiE6Ku5AUXm/op2A3CowJIYSQVspgKINSdcUsLcJa8Qyh0Ns0GhxnGg2OBp9P6zYTSwY9g7zUUm5kWFmsNdvvESRDQJQbAqLd4Oorqf3DlFYBUc7fEGeehOjuSfDLslA4/R+AZx5E6wITIbm2nfue5Quh9+kEgYMLmnI2GQXGhBBCSCvAsgw0mttmo8EazW1ULZ7B44khkbQ3LZcWC6kkDmKxX/N0mrQKGoUO2TeKkZUkR86tYui1FVm/AhEfvm1cEBDtBv9IN0icRTWcCYBOCVHOPxDfPQlR1ikI710EjzFfMk5QmAyDZ3vzwwITofPtAl1gArSBidD5dQXEMng4NG06DwXGhBBCSAuk18uNQbDqMpTKi1Aqr1ovniEKNAbApkDY0THarHgGIVWxLIvSPLVxVDhZjoL0MrP1gh2dRdyosE+EC4SiGsZs9SqIsv+FKPMkxFmnIMy9AB5TdSKnOVHmKYvAmJH5Qj7+h/o8rQZBgTEhhBDSzCoXzygfEdZq0y3aVRTPiOXWDq5aPIMQaxgDi/z0UmO+cJIcZYUas/1uflIERBuDYXd/KXh82/LNZadWQHppQ63tWB4feq8Y6AIToPftUqfn0BQoMCaEEEKaWEXxjIumUeFrNhTPiIWjYxsqnkFsplXrkXPTlCJxsxhaVcUkTL6AB59wZwREu8M/yhUyNwfrJzFoIMo9D1HWGSi7LAD45q8/XUBPwEpgzIIHvVcHY4pEYAJ0AT3AOrg26PNrDPTbRQghhDSiiuIZF7nl0qwVzxDwnSGRxkIqjYVMGg+JNAZCgUsz9Ji0ZmVFGm5UOO9OKVimIkdCLBXCP9IVAVFu8GvnCpGDwPIEBi2E9y5yk+VEOf+AZzCOLmtD+kPvE2/WXBeYwP1f7xltzA8OTDQGwo7ujfMkGxEFxoQQQkgDMS+ecdFUPCPJruIZhNiDZVgUZiqQmSRHdpIcxfdUZvtdvB3hH2VcUs0j2An8qikSBh2EeZcgyjxlDIaz/wZPb36OcqK7Jy0CY9bRHfJRW6H3iWuVgXBVFBgTQgghdcQwKiiVV00T5C6ZimfkW7QzFs+IrbRcGhXPIHWn1xqQe6sEmaaqcxpFxaoPPD7gFepszBeOcoOzp6PVc4gyT0J6bg1EWX+Dp1dabVOVoPiO1e26kPunABsFxoQQQogNKopnXDItl3a59uIZpi+xKJCKZ5B6UZZokW1aWzj3dgkYi6pzrgiIdrOsOseYXp9887QJnk4FcfqxGh9T7xZhyhFOhDagJ1iZT4M9n5aKAmNCCCHECoOhFEqlqXiGqrx4RrFFO6HQGzJpPLdcmkTSHny+9VE6QmzFsizk2Uqu0EZRlmXVOeMqEu7wCnWqqDrHGCDMuwxR5imIMk9ClHUGxY9sgj6gu9nxuoDuYHkC8NiKD3Z61zDjRDnThDlG9uCtf02BMSGEkAceyzJQqW6ipPQit1xazcUz4iGVxkAqjYdY5Ns8nSb3HYOewb1U45Jq2clVqs7xAI9AGQJMJZhdfExV51gGgoIkY35w5imIsk6DrzH/ACfOOmURGLNiZ2gjhoERO5kmyyWAcQ5oiqfZolFgTAgh5IGj1xdxK0So1JehVF6BwVBT8Yx403JpUVQ8gzQotUKHnBvFyEySI7eGqnMBUW5wdBIZA+HCZIgumybLZZ4GXyOv8TFEd08CXZ+32F4y7MuGfjqtHgXGhBBC7mv2F88w5QZLYql4BmlwLMui+J4K6VcKkZ0sR35GmdmNCYmzCP6mQNha1TlR5im47Z9k02MZnPyN+cHB98/kuMZGgTEhhJD7SkXxjEum1SKuVlM8IxxSaSycnOLh79cdOl0gDAZaLo00PMbAID+tDNk3ipF9oxgleebLobn5S7kSzO7+EgiLb0OUeQC64odg8Opg1lbn2wUsXwweo0VVBpkvlx+sDUwE4xIK0KRPu1BgTAghpNWqKJ5RMRpsrXgGn+9kGgmOMRbPkMRAKDRW4RIK+XB2lqGoSAGAsTiWkLrQqiqqzmXfLIZObb3qXECUC5yRBVHm7xBdPQXR0VMQKO8BABQPLYSySmAMkQQ6384QZ5+BQepjmixnnDBncA2nQLieKDAmhBDSKpgXz7hkyg++Xk3xjDZcOgQVzyBNpaxQza0ikXenzKzqnINUiIBoN0R19YWbLBfinNPGVSO+PwWBIsfq+cSZJ2FthWFFr/+gTOwEg1sbCoQbGAXGhBBCWiSGUUGpumY2Glx98Yw4yKSxVDyDNCmGYVF4V4GspCJkJRejxErVOeOSam7w9NTC9cx7cPjjFFBy16bz80syAIMOEJhP+NT7dmqop0CqoMCYEEJIs6tT8QzTaLBYHETFM0iT0WkMyE0pQVY1Vee8Q53hH+WEgGgv86pzBi3ENw8C1ZRbBgDGwRW6gJ7GCXNBiTB4RBlPSpoMBcaEEEKanFnxDOVFKFVXrBbPEIl8uQCYimeQ5qIs1nIpEvdSq1SdcxQgLEKLdt7J8GEvwTHnNDQYBoXnf8xPIhBDH9ANovTj3CZG7GIKhI2T5Qxe7SkQbmYUGBNCCGlULGuARpMKBZcScREaTSqsF8/owAXBxlLKVDyDND2u6lySHFnJllXnvNwVaB90C0GOV+FS/DeEhXeAwor9osyTVs+rCx8MkYMESr8e0Pj1hN6ro0WpZtK8KDAmhBDSoCoXzzAumXYFDKOwaCcWB5mNBlPxDNKcDDoG91JLuGBYVVIxqVMikCPK7xbCXa/DS3cB4tLbQF715xLmXQFPUwzWwdVsu6bT05AOeB6aIgX0eloBpSWiwJgQQkidVRTPuFipeEaGRTtj8YwYbiRYJo2DUOjRDD0mpIJaoUN2cjGykoqQm1JiUXXOr60L/CNd0O3cNPB1pWajwtawQil0Ad2gDUgAWAp8WyMKjAkhhNjMWDzjUsVKEapr1RTPCDNNkDNWkXN0bAMej24Zk+bFsixK7pUvqVaEgrsKgAUceCUIEV+Fgzsf2rYjERDtBp9wFwhMVed0uT3gcOcXy/MJHaHz62acLBeYAL1PvMUKEqR1ocCYEEKIVRXFMy5yqRHWimcI+M6QSGMhlcYai2dIYyAUuDRDjwmxxBgY5KWVGVMkkuRQFGngwCtDgPgq2jtdQYjsKtyRCgDQe0SiaPSzFufQBSbC4c4vYAUO0Pl1rSiq4dsJEDg08TMijYkCY0IIIZWKZ1yqVDwjqZriGW2NKRFUPIO0UOVV5zKT5Mi5WQyepgT+4mvoJL6CQK8r8BKmgldl8icACAtvgKfMByv1MtuuaTsSeu9Y6Hw7A0JaFeV+RoExIYQ8gBhGBaXyKpSqiipytRfPiIdE0oGKZ5AWqaxQjcwkObKT5MhLKwXLAA/J9qCn7DS8XFPB59We88vyRRAWJEEn7W22nXEKAOMU0FhdJy0IBcaEEHKfq1PxDNOXWBRIxTNIi8QwLAozyoyFNpIKUJxnfnfDxdsR4a5Z8ClLqfYcLF8IvU8naAMTjakRfl0BkaSxu05aMAqMCSHkPmMwlKK0tHxynPFfa8UzhEJvyKTxVDyDtBo6jQG5t4qRez0b7O3T8GEvoaP4MjrzFdjBXwPvMCdjCeYoNzh5OMLx8mDg+O/c8SxPAL1PPFdQQ+ffDRBJm/EZkZaGAmNCCGnFWJaBRnMbCuUlqNWXkXzjChSKW7BePKM9pNJ4SKUxkErjqXgGaRWUxRrkXMuF5vpJSPPPIEB0GXGiWxDI9GbtHn3OF0KvYLNtusBE6Hzijf8GJkDn3x2s2Kkpu09aGQqMCSGkFTErnqG6BKXyKhimzKKdWBRoWjM4nopnkFaFZVnIM4qguPAnBBkn4aU9j66iGxDw9EAN6e2ygjPQVAmMDR7tIJ/wYyP3mNxPKDAmhJAWqqJ4xqVKxTPSLdoZi2d0hJNTPHx9uoNlI8HjuTdDjwmpm/Kqc5lJcmQny5HA+x86S0wpEOLqj2PBg96rA3SBiTB4RDVNZ8l9jQJjQghpIXS6exVllGssnhFuWi6tvJRyG/B4QgiFfLi7y1BE5WZJS2fQgkn7F5prJ3GueBRyUkph0FW8ZnOdYtAev1s9VO/ZvmKyXEAPsI70IZA0HAqMCSGkGTCMBipVEjc5zpbiGeVrBwuFrs3QY0LqwaCD4N5FsMnHwL9zEs6KCxBCAwA4ntcOBoM/JC4i48S5aHf4e3gBO1YBAPQeURWT5QITKBAmjYoCY0IIaWTG4hlZZqPBNRfPiONGhB0cQql4Bml9GD2E9y5BePckkHICkoJ/IWRVVpt2i7sLXs9BcPOXViwNyLqgePjX0Pk9ZFFsg5DGRIExIYQ0MPPiGRdNxTMKLNoJBO6m5dKMFeQkko4QCGjpKNJ6idL/gOP5ryHKPguBQWHTMcE+eVAEVJlVx+NBGzG0EXpISM0oMCaEkHqoXDxDYSqgoVbfgvXiGVEVo8FUPIO0ZowBYCuWBCwtUCMrSQ7h1avoqbOeG1zO4BIKbVAit4QaI/Nr7N4SYjMKjAkhxA4GQymUyitcFTml6orV4hkikS+kkthKo8FUPIO0YiwDQUESxJknIco8BVHWaeQN2YPLN5yReb0IJXnGSaIyfhv09DE/1OASAm1gQkUgTKWVSQtGgTEhhFSDZQ3QaFJNI8HGtAiNJhWWxTMcIJFEc2sGG0eDqXgGacVYBoLCZIgyT5mC4dPga+RmTa5v24XryhEAAB6fB+8wZwREh0CVmQiee5BxslxATzAuQc3wBAipGwqMCSHExKx4hmk0mGEs8yTF4iDTaHAcpNI4ODpGUvEM0rqxLARFNyHKPAlx5imIMk+Bry6s8ZBgyVWUtJsK/yhX+LVzhdjRGFKUYVdT9JiQRkGBMSHkgVRRPONipeIZGRbtjMUzYriRYJk0DkKhRzP0mJDGI079Ca6Hn7aprUbgBbVfT4R2HwO3kLa0Zja5r1BgTAh5IOh0eRV5wbUVz5DEmNIi4kzFMwTN0GNCGhDLQlCcClHmSWgDe4FxC+d26XUMchVRqG51bBU8UOraFWjbB4KovmDcIiAUCcB3lwFFtq08QUhrQYExIeS+wzBaqFTX7SqeIZPGQyKNgVDg0gw9JqSBsSz4JWkVk+UyT0GgyAEAlCX+B0XtZiIrWY6sJDlyU0pg0DHw8wyFlygNSsYVRY5doAtKgEPcQAgDorjVU2hsmNzvKDAmhLRqxuIZ2WajwVQ8gzyI+CUZphxhYzAsKMuy2q741FH88F0XszmkUlcxUgJfQ2HbILjEdIJYJIC4ifpNSEtCgTEhpFUxL55xyVQ8I9+iXUXxDGNahETSAQKBzMoZW69SXQnSy9KMX4o0ZCjSIBYL8XjYNLRzjm7u7pEmwC/LhuzM/0GUeRKC0rs2HeOsSwVYFu6BMgREuSMg2g1ufhLwePGN3FtCWj4KjAkhLRbLslCr76C09KKpgMZlqNU3Yb14RmSV5dLuj+IZDMsgT33PFADfQboi3fhv2R0UaYusHnO7KBUb+25v4p6SRscYAL55vjsrksEheQ94bPVJDhpGiixtR2TrY6Hy7QlZhy4YFe0BiQuNCRNSFQXGhJAWo6J4xmWo1JdNo8Fyi3YioQ+3VJpUGntfFM/QGrTIVN41Bb3GEeD0MuMosNpgOUmwJg4Ch0bqJWlKfEWuKT/YmBqhiZ4AZdeFZm1YBxdo3GPgWHiJ26ZlJMjSdkCmNhb3BPFwaNsZAe09Ed7GBUIxTSQlpCYUGBNCmgXLGqDW3K5YJUJ5qYbiGe0r5QbHQixuvSVkq6Y/pJelIaMsDVmqLDBs1ZFw24n4YoQ4haCjdwdMCp3SgD0mTYWnzDOtIWwMhIXyFLP9TOZJoOtCMAyLgowyZCUZJ89FqTrATyxEpiYGmdoYaD06wj/OEwHRbmgXKAOP3/rvnBDSVCgwJoQ0Cb2+sFIp5cs1FM8IhlQaCyenOPj79YBOFwKGaV2jXCzLIk99D2lld5ChSEOaKfhNK7uDIm3NRRNq4yJyQYhTGEJkoQhxMn4Fy0LhJ/WHg0gEd3cZiooUtLZsK8BTFZgqyxmDYWHRzRrbC7P+xt97kpB5UwWtUs9t/5c/GT5+zvCPckPXaDc4udMdA0LqigJjQkiDMxbPuGk2GlxT8QxZpbSI8uIZQiEfLi7GII9hWmaQp2N0uKvIMAa9ijvcSHCGIh1qg6pe5/aT+HNBb6hTKIKdQhEqC4Obg3sD9Z40K70anhu7gcdoa28KB2Rro5Gp6Yi72bnQsVKIJQL4tXNFYLQ7fNu5cFXnCCH1Q79JhJB60+nuVZRRrrF4RoRpcpxxubTWUjyjTFdaKfWhIgBuiPSHYFkwgmQhCHUKM44Ay4xBsKOgdedME4CnlkOUdRrgi6ANG2S+U+gIvW8niLLPWhxn4DngHtMe6WXtkamNQa6uHRiI4OTpgPBObgiMdodnsBP4AkqRIKShUWBMCLELw2igUiVVKp5xCTpdjkU7Ad+5Ii9YGguJNLZFF89gWRb56jykld1BeqXR33RFGgo1BfU6t7PIGSFOYQiVhRlHfiulPwhawQcDYhuepgSirDOmHOGTEOZfAw8sdP7dLQNjANrABIiyz4LhiVAk6oi00g5IK2uPXF0kDBCDxwM8Q5wQE+2GgCg3uHhLmuFZEfJgocCYEFItY/GMLCiVl6FQXoRSeRlqdRJYVl+lZXnxDONSaS25eIaO0SFTcdds9DdDkYb0snSoDMp6ndtX4odQpzCzEeBgWSjcxe73xdJxxBxPWwpR1lljIJx1GsK8y1aXTRPmngd0KkBkDGxVpTpkJ8tRnNwN6uJ3kaVqBwOMecFCMR/+HV0REOUG/0hXOMhETfqcCHnQUWBMCOGYF8+4aFouzXK0tKJ4hjEQlkg6QiCQNkOPq1emKzMFvMZJb+VrAGcpM+uZ/iBCkDTYNPJbMQkuSBYCiZBG9B4E0rMfQ5z2mykQtuW1xEJ18wJS74UiK1mOwrvlk05lAGIgdRUjINoNAdFu8A5zhkDY8j5QEvKgoMCYkAcUy7LQatNNhTOM5ZTV6luwXjwjikuLkEnjIRIFtIgR0PL0h3SFMfgtnwSXUZaOAo1lNTx7OAmdEWIKfssnvoU4UfrDA4VlACt3PUTZZyG6d6H6w3gC6LzjIZd1QbqiA66nh6JkOx9AJtfGI1DGBcOuvpIW8ftECGkBgfG+ffvw+uuvW2yfPXs2XnnlFe77Y8eOYeXKlUhJSYGfnx+mT5+OJ598sim7SkirVlE8wxgEK1VXYDAUW7QTiXwhlcRWGg1u/uIZldMfMswmwdU//cHH0Zdb9ixEFsYFwpT+8ADSKSG6+w9EWacgzjwJsCzkj31v2SwwEeK7J7jvWR4feu9YqH16IpuJxc2cCNxN1kGvqUirEAh58GnjgoBodwREuULiTFXnCGmJmj0wLrd+/Xo4Oztz3/v6+nL/P3/+PObPn48xY8Zg8eLFOHfuHN577z2IxWJMmDChObpLSIvGsgZoNKncSHDNxTOiq5RS9rV+0iZgTH8wljzOUKYhW3MXtwpTkFnP9AchT4ggWbApAK6YBBdM6Q8PNr0aopx/4ZB9Gsg5A7fMf8AzVCyfxvL44GlKwDqYTxrVBvWC+HYcdIEJKHbuhtSiNrh7y4D8y6VgWQDQAAAcnYTwjzJOnPOlqnOEtAotJjDu2LEjPDw8rO5bs2YNOnTogOXLlwMAevbsiezsbHz66ad47LHHwOdTPhZ5sOn1RVWWS6uueEaQaTTYuG6wo2Mk+PymndzTFOkP5cuehZgmwPlL/CHgt5i3O9JcDBqIcs9DdNe4aoQo9zx4Bg23u+r9AR7LQJR9Ftqwwdw2xsAiWx2JLJcvkfWPHKX5agBybr+rrwQBUW4IaO8GjwCqOkdIa9Pi/1JotVqcPn3aLK0CAEaNGoVdu3bh2rVriImJaabeEdL0jMUzbnGT42ornlGeF1y5eEZT0DN6ZCrvciWPywtgZCjSoNQ3XPqDsQCGMQB2F3tQ+gOplstP8+GQ+pNNbfWe7aENTITBKQA6tQE5t4qRlSRH9g05tKqKuxd8AQ/eYc7GfOEoN8io6hwhrVqLCYxHjhyJoqIiBAQEYOLEiXj66achEAiQnp4OnU6HiIgIs/Zt27YFAKSkpFBgTO5rOl1eRV5wjcUzwrnCGcbR4KYpnqHQKbjcX+MawMb83yxlJgz1TH8IdQlFkDQEQdLy5c/CECwLhkTYslbAIC2EQQdh3iUI869DHTPFYrfOv3v1gbF3e6gDekLjnwBdYALK1FJkJcmR9aMceannwRgq0pDEEgH8I40T5/zaukLkSCkShNwvmj0w9vb2xnPPPYf4+HjweDz89ttv+OSTT5Cbm4slS5aguNg4OcjFxTzHq/z78v11JRBQGoY9yq8XXTfb2XPNGEYDpSoJCsUlKBSXoFRcglaXbeWczpBJYyGVxcFJVj4a3HjFM7j0h7I03Cm7g/RSY/CbVpaGfHVevc7tJHRCqHMYN+ob6mz8N8g5CB5uzigpUcFgaJkloVuaB+73k9FDcO8ShBl/QZR5CsKss+DpjHcjDJGPgJV6mjcP7QWcNP7f4NEOuqBE6IN6gQ1JgJNPMLKu5SH9aiEyj2RBnmN+V8PZyxFB7d0R2N4NXiHOD3zVuQfutdZA6LrZr6lvAjZ7YNynTx/06dOH+753795wcHDApk2bMG/ePG57dbdH63vb1MWFJt7UBV03+1W9ZizLQq3ORHHxORSXXEBJyQWUll4Dy+qqHMmHk1MUXF06wcW1E1xdOkMqDW+U4hk6RoeM0gykFqdyX7flt5FakgqFzjJn2R6+Ul9EuEYgwi0C4S7hxn9dw+Hp6Fnj7zG91ux3314zxgBkXwTu/Amk/gmknwK0ZVabusnPAYFjzTe6dgfGbwBCe0Pg7AtGa0B2UhHu/JyHO5f/grKkYuIdjwf4tXFFeJw3wuI84e4na8Qn1nrdt6+1RkbXreVq9sDYmuHDh2PDhg24fv06AgMDAViODJeUlACwHEm2F41G2Ucg4MPFRULXzQ7l16yoqAClpZeNo8FK44iwteIZQqEHZLJ4yKRxkMliIZXGmBXP0GoBrVZVrz4p9QqklVYUv0grvYO0sjvIVNytV/qDgCdAkCzYNAJszP0NdQ5HsFMIpNbSH9SAXG0935hea/a7X6+Z6PbPEF/ZAlHmGfC0pTYdo0o7D3XAEMvtzgOReVKOzKRzyLlZDIO+IkVC5CCAf6QrAk35wpWrzhUV1e+D4f3mfn2tNTa6bvZzdZU06SILLTIwriwkJAQikQi3b99G3759ue23bt0CALRp06Ze5zcYGOj19OK0F123mrEsC402zVQ04zLU6qsoK0sCUPWaVS6eEQeZNM6ieAbLok7XmmVZFGjykV5mDIC5tX8V9U9/kAmduJUfgp1M+b+yMPhLAyCsZvWHur5e6LVmv1Z7zaopqCEszoA49ZcaDzW4hEAbmABdYCJ0gQlgnAIAPQOWZVGco0JWshxZSXIUZpoHuFI3MYLauyOqmx+k3mKzBQ1b5TVsYq32tdbM6LrZjmVrb9OQWmRgfOjQIQgEAnTo0AFisRg9e/bE4cOHMX36dK7NwYMH4e3tjQ4dOjRfRwkxMRhKTMUzTEumqS7DYCixaCcS+RqDYImxipxEEl3v4hl6Ro8sZSYyuOXP0o3/KtKg0NdvlMvb0YcreVy+BnCILBQeDjWnPxBiE5aBoDAZokxjQQ1R5mkUTT5qDGor0QUkWBxqcA6CLjCRC4YZ58CKfXoGeaZVJLKS5FAWa82O9QiSGZdUM1WdE4kEcHeXoahIQcEKIQ+4Zg+MZ82ahZ49eyIyMhIA8Ouvv2LXrl2YNm0avL29AQALFizAlClT8NZbb2HUqFE4d+4cdu/ejXfeeYfWMCZNjmUNUGtuV6wSobwEjea2RTtj8YwOcHKKg69Pd7BsO/D5PnV+XKVeUWnkt/zrDjKV9Ut/EPKECJQFIUQWhhCnEK78sTH9gfIqSQNiWQgKb3CV5USZp8FXF5o1EWWehiZqnNk2g0ckdN5xMHhEVgTCLsFmbTQKHbJvFCMrWY6cm8XQaytVnRPx4dvGBQFRbvCnqnOEkBo0e2AcHh6OPXv2ICcnBwzDICwsDG+88QamTp3KtencuTPWrl2Ljz/+GN9//z38/Pzw1ltvUdU70iT0+kKLUsoMY5kXKxYHm5ZLizUrniEU8m0ejbKW/lC+BnD90x9k3IhvefpDsCwUAdLAatMfCKkXloVAngJR5imIMk9CnHkKfFXNRVxEmSctAmPweJBPPGTRtiSvIkWiIL3M7Jaro5PIuLZwtBt8wp2p6hwhxCZ1+mt48+ZNnDt3Drm5uVCr1XB3d0fbtm3RrVs3ODk52XWut956y6Z2/fr1Q79+/erSXUJsZiyecdNsNNh68QwpJJKOkJlyg+0tnmFg9MhSZiHdVPSicg5wfdMfvBy9uZLH5WkQoU5hlP5AmpxD8l64/PqCTW0NUh/oAhOgDelfbRvGwCI/vZQLhssKNGb73fwk8I9yQ2C0G9yp6hwhpA5sDoyLi4uxc+dO7Ny5E1lZWWCtZEMLhUL07dsXU6dORUKCZU4YIS2NTnevSinl6opnRBhHg00FNGwtnqHSK5FZloH8wmxcz03GnRJjAJypyICe1de53wKeAIHSIIQ4GVd/oPQH0ixYFvySNIgzT0Eb0h+Mk7/Zbp1/12oPZSReprQIY2qEwa2N1QVLtWo9cm+WICu5mqpz4ZWqzrlR1TlCSP3YFBhv3rwZa9asAQCMGDEC3bt3R8eOHeHh4QEHBwcUFxcjIyMDFy5cwK+//oqZM2ciMTERS5YsQWhoaKM+AUJsxTAaqFRJUKoqRoN1uhyLdgK+MzcKLJXGQSKNgVBQ/bKALMuiUFNQKff3Dvf/PPW9evVZKpSacn9DzSbBBUiDKP2BNAt+SYYpLeIkRJmnICjLAgCUDvg/qDs8btaWcQmFwckfgrJsMI4exhHh8kDYvV21K/crijTcqHDenVLzqnNSIfwjXREQRVXnCCENj8daG/qtYsiQIViwYAEeeeQRiESi2pojPT0dn3/+OUJDQ82KdLRENAvZPvbkyzYnlmWh02VBqbwMhfIilMrLUKuTwFqM0vLh6NiWC4Klkjg4OIRaLZ5Rkf5gCn4rTYRT6K0XGbCVp4OXMefXNPJbHgB7Ong9sOkPreW11pI0xjXjl2aaB8Kld622U0eOQ+mQzyy2i9KPgZH5wOARZXUpNgBgGRaFmQrjKhLJchTnmq/T7ezlyOULewY7gd/AKRL0WrMfXbO6oetmPw8PWZNWCrRpyOnw4cMQCm0fnQoJCcGKFStgMNR9pjwh9mAYFZTKq1CqLkNpCoStFc8QCNwhk8ZXjAZLOpoVzwCM6Q8ZCuOSZ5Xzfxsi/SFAGmhKfwhDsCyEmwwnE1H6A2k5+CXpkP7zKcSZpyEoSbPpGGH+VavbdSHW54botQbkpphSJJLlUJdV/G7xeIBXaEWKhLNX/ZY0JIQQW9kU7doTFFcmENAtLtLwWJaFVpsOpdJYQc5YROMWgKofxCoXz4iFTBrPFc9gWRZF2kLckidxa/6WrwF8T51br/5JhVIu6A2VhSHMNQyxge3hrPcEGFpekLQwLGuZ0sAXQnJ9Z42HMWJn6AJ6QBeQAF1QIvSeta8pryrRGlMkkuW4l1JiVnVO6MCHX1tj1Tm/SDc4SClViBDS9Or9znP27FkcO3YMLMuib9++6NmzZ0P0ixCOwVBaqXjGRShVV2AwFFu0E4l8uaXSjMUz2oOFENmqbFwvS0N6we+VguCGSX+oXPSiPBXCq0r6g1DIh7ur6dYZQ7fOSPPiKfMgNi2fJso8BVXcTKhjnzJrwzgFQO8aBmHxnYptIllFIByYAL13DFBLnnt51bnMJOOocNWqczI3MfyjjatIeIU6QyCkD46EkOZVr8B4x44d+OCDD9C7d28olUps2rQJr7/+OqZMmdJQ/SMPGOvFM1IBmKfCG4tntOfKKPMc2iFHo8LNsjSkFd5BRsZupJelIVN5FzpGV+f+8HkCBJrSHyomvxnTIJxE9i1NSEhz4KkKTJXljMGwsOim2X5x5l8WgTEAaMOGgCm8wU2W0/vE1RoIA6aqc6kVS6qZVZ3jAZ5BMvhHGVMkXH0lD2wOPSGkZapXYLxhwwZs3rwZsbGxAIBNmzZhw4YNFBgTm1UUz7hoWi6tuuIZQZA4xkAnbocC1h05WgYZyrtIL7iD9LI/653+IBFIEeIUUqkAhnEEOEAaCBG/9gmnhLQYigKIbv0Gx/QTEGWegrAwucbmoszTAMtYTIxT9H7b5ofkqs4lyZFzq5qqc9Fu8I90g8SZfp8IIS2XTYHx1KlT8e677yIsLMxse2lpKSIiIrjvw8LCUFZWv9vT5P7FFc9QXOKWTLNWPIPlSaEUtkMh/JHPyJCj1SGjIAcZivMo1R2vVx88HbyMVd8qFcAIdQqDl6M3jVyRVo+nlgOfdoQTal1sCKzAATq/rtAFJgAGDSCU2Pw4LMuiNF9tXEUiSY6CjCpV55xFCIgyVZ2LcIFQRCkShJDWwabAuG3bthg7dixmz56NOXPmcEu2JSYm4sUXX8QTTzwBpVKJtWvXIjExsVE7TFoPnS7XtFyaMQhWqa6BZSsqVWkY4J6eh0L4oZD1QK5eiGyNClnKXOjYmwBuVn/yGvBNqz+EOoUiuFLlt2BZKKU/kPsa6+gGeEcDedct9/HF0Pl1gS4w0VhUw7czILR9tQeu6pwpGC4rrFp1TsotqebuL6Wqc4SQVsmmdYwB4MKFC1iyZAn0ej3effddPPTQQyguLsZ7772HP//8EyzLonfv3njzzTfh4WF7adzmRmsJ2qe6NRiNxTOum9IhjIGwTpcDlgXKGCBXx0euno88vQPyGCfk6hjka+tX+lgiKF/9oWLiW0tMf6B1K+uGrpv9hEI+3E8tBc6uA8sXQefbxVRZLgE6vy52jQoDxqpzOTdNKRI3iy2qzvmEO8P/Pqg6R681+9E1qxu6bvZr6nWMbQ6MAUCv1+Prr7/GF198gVGjRuG1116Dk1PrHoGjF6d9hEI+3NykyM29gZLSi5VKKSchX2fAPT2PC4JzdXzc0wugZGx+iVnl4eBpNvGttaU/0Bth3dB1s59QyIe7Lh2l2WlQe3cBRPYFwgBQVqg2TZwrRt6dUrCMZdW5wGg3+LZ1hcjh/liSk15r9qNrVjd03ezXIgt8cI2FQsydOxfDhw/H22+/jWHDhuGNN97AiBEjGqt/pAUwFs8wLpcmV15AWskV3FUWm4JfHu7p+binE8EAsZWjbQuKy9MfgmUhXApEqJOxApyTyLlhnxAh9zOf9tCLQgAb/+iWV53LNKVIlNxr2qpzhBDSktgcGLMsizt37kCj0SAiIgLffPMNvv/+e7z33nv4/vvvsXTpUgQEBDRmX0kTYFkWas0d5JScRkrR37hTmoxMZR5y9Tzk6ngoMpR/aqvbbVNHgYQLfiuWQDOmP4gF1gJrQkhD02sNyL1VwhXb0CgqVZ3jm6rOmSbPOXtS1TlCyIPDpsA4JSUFzz33HG7fvg0AcHd3x4oVKzB27Fj069cPH3zwAR555BEsXLgQTz31FPh8moHcGhhYA7LKbuJmwQmkFl9EWlkqMtWFyNWxUDKVR4XsX9XPPP2hYgTYy9EbfB69PghpasoSLbJNawvfu21edU7kIIBfO1cERLvBr50rVZ0jhDywbMoxnjJlCpycnPDOO+9AKpXiiy++wK5du/DXX39xK1ScPn0ab7/9NmQyGfbt29foHW8oD0Kej9qgRkZZGtLL7iCl+ALSSq4jQ5mFHE0Z9PVI/+WDb0x/cAo1WwEixCkUziKXhnsCrRzllNUNXTf7Vb5mOp0B8hwVspKKkJUkR1GW+frgMncHY4pElBu8Qp0e6Kpz9FqzH12zuqHrZr8WmWN87do1rFmzBj4+PgCAuXPnYv369bh79y7Cw8MBAD179sSBAwfw+eefN15vSbVYlkWxVo50hTEATitLQ1rpLaSVpSBPI6/XuR0FjqYR31CEuoShg28UvHj+8HUIoPQHQloQg55B2tUCJJ/NRmY1VefKUyRcfKjqHCGEVGVTYBwaGoojR46gR48e4PP5OHjwIEQiEfz9/c3aicViPP/8843SUWJkYA3IUWYjQ5GGtLI0ZJSlIU1xB+llaSjVldTr3O5iD27lB+MIsLESnLejD5f+QJ92CWlZ1AodspOLkZUsR66VqnN+bV24EsyOTi1nGUNCCGmJbAqM33jjDTz77LM4cOAAxGIxFAoF3nzzTTg60qSMxqIxaHBXkY60MmPQWz4SnKHIgI7R1n6CalD6AyGtG8uyKM1TG1eRSDZWnau8+IvMVQz/KDf4R7nCO5yqzhFCiD1sCoy7deuGX375BefPn4dWq0VMTAz8/Pwau28PhGKtvCL4LbuDdEU60svuIFeVA9bGpc6sceCLTAFvhPFfUwAcKA2m9AdCWhnGwCA/rcy0vrBl1TlPD8A3LgAhMR6I6OgNuVxJd3QIIaQObJ567OzsjL59+zZmX+5bBtaAXFUO0iulPmSUGUeDS3TF9Tq3q9ABgVIfhDlHIsylI1cBzsfRl1Z/IKQV06pMVeeS5ci+UQydukrVuQgXBHmr4f7PdzAc+hXuT+yCQ6CM8oYJIaQebAqML168iPj4eLtOrFarkZGRgXbt2tWpY61RefpDelka0srucHnAdxXp0NYj/YEHFl5CPvwdXRAiC0G4awzauPVEuGsUpT8Qch+pqeqcg1QI/yhX4yoS0lLodmyGZsMhGAzGgFn17SY4LHq9ubpOCCH3BZsC4ylTpqB3796YOnUqEhMTa2ybn5+P77//Hhs3bsS0adPuy8C4WCu3CH4zytKQo8quV/qDiMfCV8jCR8QiSOKFUOe2iHDtgjae/SBzCKWRIELuMwzDovCuwrikWnKxRdU5F++KqnMeQU5gMzOg3LwKZT8fAQwGs7bqH3+AYdZswF3WlE+BEELuKzYFxj/++CM++OADzJw5E97e3ujWrRs6dOgAT09PODg4QC6XIyMjAxcuXMDly5fh4uKC5557DpMmTWrs/jcaA2vAPVWuMfjlJr8Z/y3Wyut1bic+C18RAx8hA18RiwBHN4S7tEeQSzc4yeIgkbQHn1+3ynKEkJZNpzEgN8VYdS67uqpzpvWFy6vO6dPToHj/I2h++QlgLHOHhTGxkM6YDb6nZ5M9D0IIuR/ZFBiHhIRgzZo1SElJwfbt23Hs2DEcOnTIrI2joyM6deqEZcuWYdSoURCLW8cEL41BjTsl6aaJb6YJcGXpyFCk1TP9AfAUMvARGoNgX1MQ7CsSwcupA6TSWMik8ZBKYyAS+TbcEyKEtDjKYi03ce5eagmYylXnHI1V5wJNVefEkoq3ZX3aHag2fQ3Nrz9bD4jj4iGdMRuih7qBx+PRXSVCCKknmyrfWVNYWIh79+5BrVbD3d0dAQEBXBW81iJbkY2he4bWK/1BzBPAVyyCt0ANH6EWfiIWPkIG3iIWIh4gFgdBKo3jviSO7cDjta7rVBmtY2w/umZ105qvG8uykGcrkWVaUq3aqnPRbvAOdQLfSlUn5aavofx6HWDlLVoY3wnS6U9zATG3vRVfs+ZE181+dM3qhq6b/Vpk5TtrPDw84OHh0ZB9aXIGxmBzUOwqckaAozN8hCy8+MXw4hXCR8TCXcCCb/q7xOdLIZXEQiqNNQXCMRAKW/c1IoTYxqBjcC+1hAuGVSW6ip3lVeei3REQ5QYXH8daR3eF0R0sgmJhpy6Qzngaos4P0egwIYQ0gjoHxvcjHnjwk/ojWBoAf7EY3kINPNh7cGPuQMrPBZBr1t7BIcIUAMdCKomFo2Mb8HiC5uk8IaTJqct0yL5hTJHITSmxWnUuINoN/pE1V51jWdYi0BV17wlh+47QX78KUZeHIJ0+G6LOXRrtuRBCCHnAA2OpSIoZkTPgLeDDm18KVzYDOvVV6HTHjQ3KB2v4gEDgYjEaLBDQUmmEPEhYlkXJvfIl1YpQcFdhVnVO4iziUiR8wl0gqKXqnP7WDSg3fg1BeBvIZs0x28fj8SB7/iVAr4covnNjPB1CCCFVPNiBMU+LTuovwbLGWeEVWYB8ODq2hVQazwXCDuIQ8KhgBiEPHMbAIC+tzJgikSSHosi86px7gBT+UcZVJNwDpDalOOhv3oDym6+g/fMYAID3z1lIJkwC38XVrJ2oY2zDPRFCCCG1eqADY5bRgmX1EAo9jKPAEmNahETSEQKBtLm7RwhpJuVV5zKT5Mi5WaXqnJAH3wgXLhiWutq+Ao8++TqUG7+G9sRxs+2sQgHVrh2QPT23wZ4DIYQQ+z3QgbFY7IWOHQ6Bz/eniSyEPODKCtXcqHBeWpl51TmZkAuEfdu4QORg31wCXdI1KL9ZD93JE1b3i3omQpzYu179J4QQUn8PdGAsEEjh4BBES6YQ8gBiGBaFGWXc+sIleWqz/S4+EgREuSIg2h0eQTLw+fZ/eNZdu2oMiE//ZXW/KKGXcdm1Dh3r9BwIIYQ0LLsC43PnziEnJwdt27ZFZGSkxf7c3Fzs3r0bzz77bIN1kBBCGopOY0DurWJkJRcbq84pK1ed48E7zAkBUcbJc04ejnV+HEYuR+l7b0N35pTV/eJefSCZPgui6A51fgxCCCENz6bAWKvVYt68eTh16hS3rNCQIUPw3nvvwcWlYmWGnJwcrFmzhgJjQkiLYVZ17nYJGIN51Tn/dq4IsFJ1rj54zs5gcrIttot794V0+tMQRkU3yOMQQghpWDb9Fdi0aRP+/fdfvPrqq4iLi8OpU6fw9ddfY/Lkydi4cSN8fHwau5+EEGITlmVRlKXkgmF5tnnVOScPB/hHuSEw2g1e1VSdqy+eQADJtJkoe3cJAEDct78xIG5neaeNEEJIy2FTYPzDDz9gzpw5mDlzJgCga9euGDJkCObNm4cpU6Zg8+bN8PPza9SOEkJIdWqtOhdckSLh4l171Tlb6S6cg2rfbji/8TZ4juapFw4DB0N/9TIcR42BsC0FxIQQ0hrYFBjfvXsXXbt2NdsWHR2Nb7/9FtOmTcOUKVOwadOmRukgIYRYoy7TIStZjuxky6pzQjEfvm1dERjtBr9IVzjKqq86Vxe68+eg/OYr6M7/a+xLTBwkEx83a8MTCuH04qsN+riEEEIal02BsUwmg1KptNgeFBSELVu2YOrUqZg6dSpeeumlBu8gIYQAlarOJRUhK1luWXXOpbzqnDt8wpxrrTpXl8fXnfsHym/WQ3/xvNk+5dbNcBzzKHgOdZ+wRwghpPnZFBiHh4fj7NmzGDBggMW+wMBALjh+8803G7yDhJAHl8HAIOdWMTKuFVVbdS4g2ri+sJu/bVXn7MWyLHT//g3lN19Bf+miZQMeD+IuD4FVKu/bwJhhGBgM+tobtlIMw4NaLYBWq4Gh0uRMUj26ZnVD182SQCAEn99yKgvbFBgnJiZiy5YteP755+HoaPnGHxgYyKVVZGZmNngnCSEPDq1Kj+wbxci+IUfOzRJoVRUBGV/Ig0+4CwLbu8E/yg1SF9urztmLZVno/jlrHCG+bCUg5vPhMOhhSJ6aCWFoWKP1ozmxLIuSkkKoVGXN3ZVGl5/PB8PQmvb2oGtWN3TdLEkkTnBx8WgRxdZsCoynTp2KwYMH19gmICAAu3btwq1btxqkY4SQB0dpgZqbOJefVgq20t8MRych/CNNVefaukAotq/qXF1oz/8L5bq10F+5bLmTz4fDkKGQTpsJQUhoo/elOZUHxU5O7hCLHVrEH63GIhDwaATPTnTN6oauWwWWZaHValBWVgQAcHX1bOYe2RgYOzk5oV27drW28/DwQPfu3evdKULI/Y1hWBRklHElmEvzzavOufpIENjBHe27+0PsKoCBado/IoaUW5ZBsUAAhyHDIJ02A4LgkCbtT3NgGAMXFDs5udR+QCsnFPKpCqqd6JrVDV03c2KxAwCgrKwIzs7uzZ5WYfdq9teuXcNff/2F7Oxs8Hg8+Pn5oVevXujQgSo4EUKqV151LjNJjpwbxVaqzjmbJs+5wcndAUIhH+7uMhQVKYAmDowdR42B6ttNYAryjQHx0BHGgDgwqEn70ZwMBgOAij9ahBDSWMrfZwwGPfj8xkuRs4XNgXFhYSEWLVqEEydOgGXN/0h9/PHH6Nu3L1asWAEPD48G7yQhpHVSFmuQlVSMrKQi3Esttaw6F+mKgKiGrTpnK5Zlof3rT/CEQoh7Jprt4zk4QvLUTOiTk4wBcUBgk/atJbmf0ycIIS1DS3qfsekvkUqlwlNPPYXU1FRMmDABgwcPRlBQEFiWRWZmJn755Rfs27cPM2bMwM6dO61O0COE3P9YhkVRtpJLkZDnWFadKx8V9gppnKpztfaRZaE9cRzKjV/DcCMJguAQiLr1AE9gnrsseXR8k/eNEEJI87IpMN68eTMyMjKwefNmdOnSxWxfmzZt0LdvX4wePRqzZ8/G5s2bMWfOnEbpLCGk5dHrGNy7baw6l50sh6q0ouocjwd4hlRUnXP2ariqc/ZiGQbaP49BuXE9DLductsNGenQ/PozHB8e1iz9Ik3j6NHD2L17O9LT08CygLe3N2Jj4zF37gK4uzfdnc5nn50DqVSKDz/8pEkeb9u2LVi79lOcOPFPkzxeQ7l7NwPbt2/B1atXkJqagpCQUGzZssvm4w8fPogtW75BTk42AgODMGPGHAwcaLmIwLlz/+Dw4YN4882lZtuPH/8D+fl5GDduQn2fis2efXYOLlw4Z7F969Y9CK1m5ZtPPvkIe/bswLhxE/DSS4tqPP+hQwewfPkyi+1PPvkUnnnmOe777du/xdGjh5CdnQW9Xo+AgECMGTMO48ZNbFEju43FpsD48OHDeOqppyyC4sq6du2KqVOn4tChQxQYE3KfU5XqkH3DOCqcm1ICg8686pxfW1cERLvBP9IVDg1cdc5eLMNAe+x34wjxbSur5ohEYO7lNn3HSJPZsmUj1q1bg4kTn8CsWfPAsixSU1Nw9OgR5OfnNWlg/PLLiyFohjslrU1qagpOnfoLHTp0BMsydi1v9vvvv+D995diypTp6N69J44f/wNvv/06nJyc0L17T+j1emzbthnjx082O+7Ysd/h6emJmJg4/PnnH0hKutakgTEAxMbGY8GCF8y2+fn5W22bknILP/74A2QymV2P8b//rYJM5sR97+3tbbZfoSjDkCHDEB4eAaFQhH///RuffPIRFAoFpk2baddjtUY2BcZpaWno2bNnre0SEhKwZcuWeneKENKysCyL4lwVspKNwXDhXYXZfqmrGP6mUWGfcGcIhM3/h59lGGj/+BXKTRtguJ1i2UAshuPIMZA8OQ0CH9+m7yBpMnv37sTw4SPx3HMvctsSEnrhiSemNch6shqNGg42FncJD4+o9+M9CHr16os+ffoDAN5/fymSkq7ZfOz69V9gwIDBmDfvWQBAly5dkZ5+B+vXf4Hu3Y2xjEwmw7PPzkGXLl2Rl3cPixa9CBcXV8ye/Uy9+37o0AFs2LAOe/YcsPtYZ2dnxMTE2tR25coPMXnykzh8+KBdjxEV1R5ubm7V7n/66Xlm33fr1gO5uTk4dOjgAxEYN/9fL0JIi2TQM8hNKca5H9Nw6ONLOLrmKq78kskFxe6BUnQcGIAh8zvikZfj8NCoUPi3c232oJg1GKD55SjkTz2O0rfftAyKxWI4jp8E9x374PTiqxQUPwDKykrh6elldV/lpaF69+6KbdvMB3e2bduC3r27ct+fO/cPevfuipMnT+Ctt17Dww/3w3/+sxjvv78U06ZNsjj/iRPH0bt3V6Sm3gZgvF3+2msvmJ0rKem62TEGgwGjRj2MtWs/5bbduZOKxYtfwtCh/TB4cG+8+urzyMy8a3acQlGGd99dgiFD+mLkyMFYu/ZTbnURe+j1eowePRTr1q212Ld06ZuYOfNJu89pr7ou2ZWVlYm0tDsYPHio2fYhQ4bh+vWrkMvlEAqFeOyxSfjww09w7Nhv+PffvzFo0FC8+eZS+Pj44v33l+Lw4YNITb2N3r27onfvrnj//aUN8KwaztGjh5GVlYknn3yqSR7P1dX1vq5+WZlNI8ZhYWE4ffo0EhISamx36tQphIeHN0jHCCFNT6PUI+emcRWJnJsl0Gkq/qgKhDz4RLgYUyQauepcfbBKJco+WgFWYT6qDbEDHMc8CukT08D3sh4kkftTVFR77N+/DwEBgUhM7F1tkGyP//u/5Xj44eFYvnw8+Hw+9Ho9Dh8+iNu3byEioi3X7tdfj6JNm3ZWR4o7deoCb28f/PLLT4iObs9tP3fubxQVFXLBXWbmXcybNxMREW3wxhtLwefzsHnzBjz//DPYtm0vxGLj7+KKFe/gzJnTmDfvWQQEBGDfvt24VSmfvtz48aPg5+eP1avXWX1uQqEQw4ePxOHDB/H00/O4ILW0tBTHj/+BBQuer/Ha6PW1B1B8Pr9R1qtNS0sFAISFmcciYWHhYFkWaWl34OwciwMHvsOBA/vRt+8ApKTcxK+//oR//z2L2bOfwfTpT0MuL0Ja2h0sWfIeAMDd3b3B+2rN+fPnMHhwbzAMgw4dYvD00/PQqZN5GqtSqcCaNZ9i4cKX6rTYwdSpE1FcLIevrz9Gjx6LJ56YBoHAsnCSXq+HVqvFhQvncOTIj5gxY3adn1drYlNgPHz4cHz++efo378/OnfubLXNuXPnsGXLFjzzTP1vQxBCmg5XdS5Jjvx086pzDjKhcRWJKDf4tmmaqnP1xXd2huP4yVBt+tq4wcEBjmMfg/TxKeA3QED0oGNZFiyrrr1hI+Hx7J/A+fLLi/DGG6/igw+MQY6/fyB69eqDSZOegL9/QJ360bt3P7MJSwaDAe7uHvj5558wd64xMNZo1Dhx4ni1t5/5fD4GDhyC3377GQsWPM89r59//gkhIaGIjIwGAHzzzVdwdnbBypVr4OBgXO81JiYeEyeOxsGD+zFu3ATcuZOKY8d+x6JFb2HkyDEAgG7demLSpLF1en6jRo3Ftm2bcebMKSQk9DL16wgA4+hrdbKzszBhwuhazz9jxmzMmjW3Tn2rSWlpKQBjYbLKnJ1dTPuLwTAMSkpKsGrVl0hKuoaSkmK8+eZSHDv2G7KzsxAbGw83N3fk5GTblNbAMOY50OX/r/oBQSisOeTq3PkhDBv2CIKDQ5Cfn4ft27/FCy/Mx+rV6xATE8e1+/rrdQgKCsagQQ/X2rfKPD29MGvWXHToEAMej4cTJ47hq68+R17ePYuJe3fvZmDy5Ee57596ahYmTWr8OwUtgc0loQ8cOICpU6diwoQJGDRoEIKCjAvd3717F7/88gv27NmDiIgITJs2rVE7TAipn1qrzvlKuFUkPAJl4PFb5ixkVq+H9uQJiPv0swiUJBMnQ33gezg+PBySx58E36P5y4zeD1iWRcrt6VAqLzZbH6TSTmgT8Y1dwXFERFts2bIL//xzBmfPnsGFC/9iz54dOHToANasWYd27aLs7kd5sFhOIBBgwIBB+O23nzF37gIAwIkTf0KtVmHIkKHWTgEAGDJkKHbu3IpLly4gPr4zdDodjh//A5MmPcG1+fvv0xg06GEIBAIu2HJ2dkbbtu243Nvr16+CZVn07TuAO04oFKJPn37Ys2en2WPakvsaFBSMzp0fwo8//sA91x9//AH9+g2Ai0v1lRC9vLyxfv3mWs/v5eVda5v6qPr6qKi/wINIJLL6YaVfv4F1eqxvvvkK33zzlcX2/v3N52bt3v1DjR/Eqn5QSEzsg6lTJ2LjxvX46KPPAACpqbfx3Xe78eWX39jdzx49EtCjR8Wd/+7de8LBwRG7dm3DtGmz4FXpTpqPjy/Wr98MpVKJixfP49tvN4LP5zfKh5mWxqbAWCKRYOPGjXjttdewfft27Nixw2w/y7Lo1asXPvzwQ1rDmJAWSKcxGFMkkuXIvlEMbZWqcz7hztzIsMy9ZVc6Y/V6aI4egXLLN2DuZsDlw5UQVwlS+C6u8Ni9Hzxxy0z3aN1a5gel2ohEIiQk9EZCQm8AwJkzp/Daay/gm2/WY/ny/7P7fNZurQ8ZMgz79u3GtWtX0KFDDH755SfExMRWu6oAAERHd0BQUAh+/vknxMd3xunTf6GsrNQsR1Yul2PXru3YtWu7xfFisfFvbn5+PoRCoUXQWp8VN0aNGovly5dBLpcjPz8PycnXMX/+whqPEYlEaNs2stZzN1bZX2dnZwDGkWOPSh+Iy8pKTfvNr0+XLl3RpUtX1MeYMePQq1cf7vu//voTP/zwHT744GOzdvZ+GJBIJEhI6I0//viV27Zq1UoMGDAIfn4B3Og4wzDQ6fQoLS2FTCaz69oOHDgY27dvwc2byWaBsVgsRnS0saJxly5d4egowRdfrMLYsY81SCpSS2ZzqSlPT098/fXXuHLlCk6ePImsrCwAQEBAABITExETE9NonSSE2E8h13CrSORVqTonlgjg184VgdHu8G3nArFj01adqwtWr4fmp0NQbv4GTFYmt135zXqIeiZajBBRUNzweDwe2kR80+pSKazp0SMBbdq043JSAWMwoNfrzNqVlpZU0w/LPsTExMHPzx+//noUoaFhOHPmZK35uIBx1Pi77/bghRdewS+/HEVUVHsEB4dw+11cXJGQ0Mvq0mFSqRQA4OXlBb1ej5KSErPguKiosNbHr06/fgPxySf/h6NHDyErKwv+/oG1BpHNnUoRGmrMLb5zJ9Vs7d87d1LB4/GqXQ+4Pry8vM2C3tu3UyASibjAsj6qVhpOT7+Ds2ez8dNPh822G3Omv6txzWNbzl+dqKhoGAwGZGdnU2BcVUxMTKMFwQqFAsOHD0dubi727NmD2NiK3J5jx45h5cqVSElJgZ+fH6ZPn44nn3ww8l0IsQXLsCjKUiAr2Th5Tp6jMttvXnXOGXxB6xj5Y3U6qA8egHLLRjDZWRb7DXdSwWTehSAouBl69+Dh8Xjg8STN3Q27FBYWmI0eAsb833v3cs0mxXl7+5gFygDwzz9nbH4cHo+HQYMexk8/HUJ4eBsYDAYMHDik1uMGDx6Kb775Cn/99SdOnvzTImDs2rU7UlNT0K5dlNVJUoBx5JnH4+H48d+5HGO9Xo8//zxmc/+rEovFGDp0BH744XsUFRVgwoTHa/1Q0typFAEBgQgNDcNvvx1Fv34VaSU///wT2rfvWOMyZZUJhSJotdpG6aOtVCoVTp06gfbtKwLspUuXQ6vVmLVbuvRNdOwYiwkTJsPX18+ux/j1158hEAgQGVlzOtGlSxfA4/EQEFC3nPzWpF7DRFqtFnv37sXNmzfh4+OD8ePHmw3F22vt2rVWl5Y5f/485s+fjzFjxmDx4sU4d+4c3nvvPYjFYkyY0LSLbxPSkui1Bty7XcqNDKvLrFSdM6VINGfVubpgdToU7dyFvM+/AJOTbbGfJ5XBccIkSCY+Dr6LazP0kLQW06ZNRq9efdC9ewK8vLyQn5+HPXt2orhYjgkTHufa9e8/CLt3b0f79h0RHByKI0d+REFBgV2PNWTIMGzdugnr13+Bhx7qZlMqQ2hoGCIjo7By5YdQq9UWk6pmzZqLp5+ehpdeeg6jRz8KDw8PFBYW4Pz5c4iP78QVY+jTpz8+++xjaLVa+Pv7Y9++3VbXaZ40aSz8/Pzx6aef19q3UaMexa5d28Hn8zFixKha2zfUSKlarcapUycAADk52VAoFPj9918AAJ06PcSlsqxY8Q6OHPkRx45VfICZNWse3n77dQQEBKFbtx44ceIY/v77NP73v1U2P35YWBgOHfoBP/98BMHBIXB1davzRE1bXLx4Htu3b0HfvgPg5+eP/Pw87NjxLQoLC/Duu//l2lmbDCgWi+Ht7W02mn/+/L944YX5WLz4Pxg+fCQA4KWXnsVDD3VDREQbAMalBH/44TtMmDCZGwUuKyvDq68uxMMPj0BQUBD0ej3+/fcf7NmzA2PGjLP4gHk/sikwXrduHY4ePYo9e/Zw2/R6PR5//HFcu3aNG4rfunUr9uzZA19f+9cFTUlJwbZt27Bo0SK8/fbbZvvWrFmDDh06YPny5QCAnj17Ijs7G59++ikee+yxRstVIqQlUpXqkG0KhHNvt+yqc3XBarVQHzoA1dZNYHJyLPbzZDI4jp8MycTJFBATm8ycOQd//fUnVq9eCbm8CK6ubmjTph0+/fRzs2Bi+vSnUVRUiA0bvgKfz8fo0Y+ibdt2+Pxz2wOqtm2NS7Olpt7GnDnzbT5u8OChWLv2M24Jt8qCgoLx1Veb8NVXn+Pjj/8LlUoFT08vxMd3Rps27bh2r7++BCtXfojPP/8MYrEYw4aNRFxcZ3z55Wqz8xkMBpvXNw4Pj0BwcAgCA4Pg04RrfhcVFeI//1lstq38+88++wLu7safG8MwFs9l4MDB0GjU2Lx5A3bs+BaBgcFYtmwFV9zDFiNHjsG1a1fxySf/h+LiYgwfPtKibHRD8vT0glarw5dfrkZxcTEcHSWIjY3DK6+8jg4d7L9Lz7IsDAaDWapESEgYDhzYj7y8XLAsi+DgECxc+DLGj69Yf1ssFiM4OBQ7d25FXt49ODg4IjAwCK+++gaGDXukQZ5rS8djbUgwmTp1Ktq2bWsWsG7duhXvvvsuRowYgTlz5iAlJQVvv/02Ro8ejSVLltjdkZkzZyIqKgr9+/fHtGnTuFQKrVaLLl264JVXXsH06dO59mfPnsXUqVOxd+/eeqV2FBUpoNfXv/LRg0Io5MPdXUbXzQ71vWZc1bkkObKSrVedK0+R8A5rGVXn6kPx5Vqovt1osZ3n5AzJxMlwHD8ZfNMEG2KuIX8/dTotCgqy4enpD5Ho/s/XFgr59J5mRWbmXUye/Cjeffe/6N9/kNk+umZ1Q9fNUk3vNx4esiYto27TiPGdO3csUhZ++eUXODs7Y/ny5XB0dER0dDRSU1Nx4ID9JRCPHDmCpKQkfPbZZ7h69arZvvT0dOh0OkREmC+O3ratcZ3IlJQUmvhH7jsGPYO8OxUpEkq5ea6be6AUAVHuCIx2g6ufpFWlSNTG8dHHoNq5FdAZ00J4zi6QTHwcjuMngV9lbVJCSOMoLpYjIyMdGzZ8BT8/f/Tu3a+5u0RIk7ApMJbL5fD3r1huhmEYnD9/HomJiWbLs3Xq1Anr1lmvpFMdlUqF//73v3jppZcsFuQGgOLiYgCwWIKm/Pvy/XXVlJ9C7gfl14uum+1svWYahQ5ZN4qReb0IWTfk0GsqRhQEQh63ikRAdMutOmcPVq0GU1ICgY/5bWNhgD+ko8ZC9ctReM2cDuHox8BKpM3Uy9alIX8/Geb++bBVm/LPlTweYOMk/fveX3/9iRUr3kFQUDD+8593LIpT0DWrG7puNRMIeBBWuevZ1OM+NgXGbm5uZgHojRs3oFarLUZqHRwcqp0xW53PP/8cnp6eGDduXI3tqhsRq+9ImYtL65pd3VLQdbOftWsmz1Ui9VI+7lzKR/YtudkbpdRFjLBYT4TFeyMo2h2iVlB1zhaMWg35zp3IX78ejtHtEfKV5Ydp51dfBO+NRRA4yZqhh61fQ/x+qtUC5Ofzrf6hul/RB/4Ko0ePwejRY2ptR9esbui6mWMYHvh8Plxdpc1eD8OmwDgyMhIHDx7E4MGDARjTKHg8Hrp3727WLj09Hd7eti/BkpmZiQ0bNmDNmjUoKysDACiVSu5fhUIBV1fj5JqqI8MlJca1JWuqwGOLkhIVDAbK9bGVQMCHi4uErpsdKl8zndaA/PRSZF6X4+71Iouqc25+UgS2d0Nge3d4Vqo6V6ZQAwprZ289WLUayu/2QPHtFjCFxpn+irw/kXviDMQdq6ZDiSFg+HAB/Y7aoyF/P7VajWliE3vf50PyeMZrZzAwNIpnI7pmdUPXzTqDgQXDMCguVkKlMp9M6eoqadJFFmwKjKdPn47Zs2dj2rRp8Pb2xk8//YR27dqha1fzhb6PHz+O6Ohomx/87t270Ol0mDNnjsW+adOmIT4+Ht9++y1EIhFu376Nvn37cvtv3boFAGjTpo3Nj2eNwcDc92/6jYGum+10aj3u3SxF8t/ZyEqWQ1vpl54v4ME7zJmbPCdzq6g6Z2BYgGn975ysSgXV93uh2v4tWCvFBhQ7toH/9nvVHk+vNfs1xDUzGFr/a89W5QEKBSq2o2tWN3Tdambtg3hTXyubAuM+ffrg7bffxldffYXLly+je/fuWLZsmVmbvLw8nD59Gq+88orND96+fXts3my+EPj169exYsUKLFu2DLGxsRCLxejZsycOHz5stirFwYMH4e3tjQ4d6r9eIiENTSHXGFeRSJIj745l1Tn/SGMg7NfWFSLH+yNFoipWqYTquz1Q7dgKVl5ksZ/n7gHJE1MhGVNzGhUhhBDSVGwu8PH444/j8ccfr3a/t7c3zpyxvUIQYEyD6NGjh9V9HTt2RMeOHQEACxYswJQpU/DWW29h1KhROHfuHHbv3o133nmH1jAmLQLLsCjMUnDBcHGuedU5N18p/CJd4NfOtVVVnasLRqmAep8pIC6WW+zneXhC+sRUOI4ZB14z55IRQgghldlV+U6tVmPIkCFYtmwZBg4c2Fh9stC5c2esXbsWH3/8Mb7//nv4+fnhrbfeoqp3pFmVV53LTCpCdnKxRdU5r1Bn+Ee5IqSjB0IjvR6ItZ8ZRRmKJj9mdYSY7+kFyZPT4Dh6LHgOFBATQghpeewKjB0dHaHRaCCRNN6KBD169EBycrLF9n79+qFfP1pHkTQvVakWWcnFyLZWdc6hctU5NzhIjb9eD8qMfgDgy5wg6tIV2t9+rtjm5W0MiEeNBc/BoYajCSGEkOZlV2AMGMsxnzp1CgkJCY3RH0JaFJZlUZyj4gptFGZWqTrnJkZA1P1Tdc4erEZjNdCVTp8J7e+/gO/tDcmTT8HxkdEUEBNCCGkV7A6M582bh+eeew5isRgPP/wwvL29LdYSdnNza6j+EdLkuKpzpnxhZbF51TmPIJkxGI66/6rO2YIpLYFq9w6o9+2G29r1EISEmu0XhreBy/99AlHnh8ATt/5CJOT+cPToYezevR3p6WlgWeO8mNjYeMyduwDu7h5N1o9nn50DqVSKDz/8pEkeb9u2LVi79lOcOPFPkzxeQ0pPT8Mnn3yES5fOw9FRgsGDh+KZZ56FQw2pWNnZWZgwYbTVfSKRCL//foo79969O/Hvv38jJycbrq5u6Nq1O+bMmQ9PTy+LY7/++kv4+wdgxIhRZtt37dqG4OAQJCT0rscztU/v3l0ttnl4eOKHH37ivv/77zM4eHA/rl27gsLCAvj5+ePhh4fj8cenQlzD+7LBYMCOHd/i1Km/cOfObej1BrRp0xYzZsxG167mS/S+997buHLlEvLz8yAUitCmTVs89dQsdO/es+GebDOwOzAuL8SxevVqrFmzxmqb69ev169XhDQxjVKP7BvGQDjnVrF51TkRH74RLsYUiShXSJwfzGCPKSmGatcOqPfsAKswjpwrN2+A81vLLNqKe9AdJdJybNmyEevWrcHEiU9g1qx5YFkWqakpOHr0CPLz85o0MH755cVU3MEGpaWleP75Z+Dn54f33vsQRUWFWL16JUpKirFkybvVHufp6YUvvvimylYWr7yyEJ07VwSUf/99GhcunMPo0Y+ibdtI5OXdw4YN6zBv3kxs2rQDUqkUGRnpOH/+X4waNZY7Tq/XY9eu7Rg5cgxcXFywa9d2JCb2btLAGADGj5+EwYOHcd+LRCKz/fv374NarcLMmXPg6+uH5OQkbNiwDjdv3sB7731Q7Xk1Gg22bPkGw4aNxOOPT4VQKMShQwfw4osL8N//foxevfpwbfV6PR5/fCoCA4Og0Whw8OB+vPrq8/jssy8QH9+54Z90E7E7MF6wYMEDN0JG7k8leRUpEgXpZWZrJTo6iYxrC0e5wSfCGcL7pOpcXTDFcqh2bYd6zy6wSvNUEs3PP0H61CwIgkOaqXeE1G7v3p0YPnwknnvuRW5bQkIvPPHENDBM/SfEajTqGkcxKwsPj6j34z0I9u/fi9LSEnzzzTbuLrRAIMQ777yFadNmIiws3OpxYrEYMTGxZtvOnfsHZWVlGDKkIpAcNGgoxo2baBbPtGnTDtOnP45jx37D8OEj4erqijt3bmPhwnlwd/dAQUE+vv9+LxITe9c46mqr999fCgB4882ldh/r4+Nn8Twre/nlxXB3d+e+79KlK4RCIT799CPk5GTDz8/f6nEODg7YtesHs+Jp3bv3REZGOnbs+NYsMF669H2zY3v2TMSECaNx5MihByswfu655xqjH4Q0OsbAIj/dlCKRLEdZgcZsv6uvBAHRbgiMdoN7QEXVuQcVI5dDtXMb1Ht3gVUpLfbz/QMgnTYDfP+AZugdIbYrKyu1enscgNmSn717d8X8+c/jiSemctuqpiKcO/cPFi6chw8//ASHDv2As2fPoFOnznB1dUNy8nVs3rzT7PwnThzH4sUvYcuWXQgPjzBLpSg/1/r1WxAd3Z47xmAwYOzY4Rg+/BHMn/88AODOnVR88cUqnD//LwwGAzp3fggvvPAqAgODuOMUijJ8/PGHOH78Dzg4iDFixCg4O7vafb30ej3GjXsEI0eOwZw58832LV36JtLT72DDhq12n9cep0+fRNeu3c1SM/v3H4j//leMU6f+qjYwtubnn3+CTCYzC+qspXy2adMWAoEA+fl5AAAXF1csXPgyzp37B6+8shCOjhKsWfMV9+Fm/PhRyMnJxr59u7Fv324AwBtvvG2RbtEcKgfF5SIjowAA+fl51QbGAoHAoqIwj8dDu3aRuHTpQo2PKRAI4OzsDINBX7dOtxB2B8aEtCZatR65N0uQmVSEnJvFllXnwp25keHKVeceZExREVQ7t0K1bzegUlns5wcGQTp1BhyGDgdPSG8hpOWLimqP/fv3ISAgEImJvasNku3xf/+3HA8/PBzLl48Hn8+HXq/H4cMHcfv2LUREtOXa/frrUbRp087qSHGnTl3g7e2DX375ySwwPnfubxQVFWLw4KEAgMzMu5g3byYiItrgjTeWgs/nYfPmDXj++WewbdtebvRyxYp3cObMacyb9ywCAgKwb99u3Lp10+Jxx48fBT8/f6xevc7qcxMKhRg+fCQOHz6Ip5+ex314KC0txfHjf2DBgudrvDZ6fe2BEZ/Pr7EOwZ07qXjkEfNcYbFYjICAIKSlpdZ6/sp9OXbsN/TtOwAOtUwCvnLlEgwGA0JDjUF3aWkpNm/egKSka+jdux9kMiesWPEO+vTph4kTH8fy5f+HV199HrGxnTB58hQAMPug0pi2bt2IL79cDYlEgu7dEzB//vPw8/Or8ZiLF89DIBAgKMi+O3wMw+DKlUtWP4ywLAuDwQCFogw//ngAGRkZePXVN+w6f0tDf9XIfUdRpOFSJO6lloJlHryqc3Wl/vEAyj75P0CtttjHDwqGdNoMOAwZRgHxA8zAGpBScgsag+VrpCk4CBzRxqUtBDzbf3dffnkR3njjVXzwgbH0uL9/IHr16oNJk56Afx3vePTu3Q/PPFNxB9VgMMDd3QM///wT5s41BsYajRonThzHtGkzrZ6Dz+dj4MAh+O23n7FgwfPcbf2ff/4JISGhiIyMBgB8881XcHZ2wcqVa7jgLiYmHhMnjsbBg/sxbtwE3LmTimPHfseiRW9h5MgxAIBu3Xpi0qSxdXp+o0aNxbZtm3HmzCkkJPQy9esIAJilJFRV0+S3ymbMmI1Zs+ZWu7+0tAROTs4W252dnVFSUlLr+cudPv0XSkqKa+wzYAygP/vsfwgJCUViojFfuKioEEFBwZg/fyE2bFgHf/8AvPzyIuzatR0ajQaRkdEQicTw8PCoMa2hnMFgAMuyAPjQ6xnT/80/SPB4PAgENb+2hw17BImJfeDh4YHbt1OwadN6zJ8/Cxs3brcY7S2Xk5ONbds2Y/jwkXYvkLBnz06kp6dZDXgPHtzP/V5JJFK8885yxMTE2XX+lob+upFWj2VYFGYquBSJqlXnnL0cuVFhz2Cn+7rqXH0JQkItgmJBcAgk02bCYfDDFBA/4HSMDs+fegZJxdeatR/Rrh3wacLnEPFFtTcGEBHRFlu27MI//5zB2bNncOHCv9izZwcOHTqANWvWoV27KLv7UB4slhMIBBgwYBB+++1nzJ27AABw4sSfUKtVGDJkaLXnGTJkKHbu3IpLly4gPr4zdDodjh//A5MmPcG1+fvv0xg06GEIBAIuiHJ2dkbbtu2QlGT8WVy/fhUsy6Jv3wHccUKhEH369MOePebpHXv2HKj1+QUFBaNz54fw448/cM/1xx9/QL9+A6oNvgDAy8sb69dvrvX8Xl7etbaxPp2JrWa7dUePHoGHhyceeqhbje0+/vgD3L6dgjVrvoLQ9D4XEhKKkKqr7giFZqk29nj++Wdw4cI5i+1HjvzI/d/Pz7/Wn89blSY8d+rUBXFxnTBr1hQcOPAdnnzyKYv2SqUSb7zxKtzcPLBgwQt29fn8+X/x+eef4fHHp6BTpy4W+/v06Y927SIhl8vx228/Y8mS1/H++/9n8fvRmtBfOdIq6bUG5KaUICtZjuxkOdRllT5x8wGvkIoUCWcvqrJmK1FsHERdu0P3z1kIQkIrAuJaRjDIgyFbmdXsQTEAJBVfQ7YyCyFOobU3NhGJREhIqFg94MyZU3jttRfwzTfrsXz5/9ndB2s5nEOGDMO+fbtx7doVdOgQg19++QkxMbHV5nMCQHR0BwQFheDnn39CfHxnnD79F8rKSrk0CgCQy+XYtWs7du3abnG8WGx8f8vPz4dQKLQIWuuz4saoUWOxfPkyyOVy5OfnITn5OubPX1jjMSKRCG3bRtZ67prSKADA2dkFpaWlFttLS8u4VIfaKJVKnDz5J0aOHFvjKOyGDevw448/4P33P0R0dAerbWoa3bbVa6+9AaVSCYGAD4OBwYYNXwEAZs6czbURieyf1Ne2bTsEB4ciOTnJYp9er8dbb72G/Pw8fP7513B2thyFr86tWzfx+usvo0+f/njmGes/dzc3N24EumfPRMjlcqxd+ykFxoQ0BVWJ1pgikSzHvZQSGPQVKRIiBwH82hmrzvm1c+WqzhFLhvw8qLZtAU8shmzesxb7pXPmg3lkFMQDBlNATMz4SwMQ7dqh2YPjaNcO8JfWb9Jnjx4JaNOmnVm+qlgshl6vM2tXWmr9tr211ZliYuLg5+ePX389itDQMJw5c7LWfFzAOGr83Xd78MILr+CXX44iKqo9giut9OLi4oqEhF4YN26CxbFSqRQA4OXlBb1ej5KSErPguKiosNbHr06/fgPxySf/h6NHDyErKwv+/oHo0sVyDd3KGiqVIiws3CKXWKvVIivrrkXucXWOH/8darW6xjSKfft2Y8OGdXj11TfQu3fjVtcNCQkDYKyGqtczcHU1ToysLhi3D2uxhWEYvPvuf3DlymWsWvWlXfnPmZl38fLLzyIyMhr/+c87Nq9GFhUVjX/+OWPz47REFD2QFotlWchzVKYUiSIUZZqvjCBzE8PftIqEV+iDVXWuLgx596DauhnqA98DWi0gdoBk/GTwvcwnIonadwDaN8QbNbnfiPgirEr8stXlGBcWFsDDw9Nsm0ajxr17uWaT4ry9fSyCMXv+yPN4PAwa9DB++ukQwsPbwGAwYODAIbUeN3jwUHzzzVf4668/cfLknxYBY9eu3ZGamoJ27aKqHfmMju4AHo+H48d/53KM9Xo9/vzzmM39r0osFmPo0BH44YfvUVRUgAkTHq81QGqoVIqePROxadPXKC6Ww9XVDYAx0NVqtTaPRv78808IDAxCx44xVvf/8stP+OST/8PTT8/DmDHjbDpnVUKhCFqttvaGjejmzWRkZKRjxAjzDwzlK5R8+OEniIqKtvl8BQX5ePHFBfDw8MSKFR9ZrJFck8uXLyIgINDm9i0RBcakRTHoGeSlliIzyZgiYVZ1jgd4BMq4FAlX3wev6lxdGHJzodq6CeqD+wFdpdEwrQbKbZvhtPCl5uscaXUEPAEiXe3PyW1O06ZNRq9efdC9ewK8vLyQn5+HPXt2orhYjgkTHufa9e8/CLt3b0f79h0RHByKI0d+REFBgV2PNWTIMGzdugnr13+Bhx7qZlMqQ2hoGCIjo7By5YdQq9UYNOhhs/2zZs3F009Pw0svPYfRox+Fh4cHCgsLcP78OcTHd8KQIcMQHh6BPn3647PPPoZWq4W/vz/27dttdZ3mSZPGws/PH59++nmtfRs16lHs2rUdfD7fpmXIRCJRg4yAjhnzGPbu3YXFi1/G9OlPcwU+Hn54uNnqCCtWvIMjR37EsWPmH2CKiorwzz9nMGXKdKvnP3/+X7z33tuIi+uErl174MqVy9w+d3d3m0dXw8LC8O+//+Dvv0/D2dkF/v4BXCDfGLZt24KsrEx07twF7u4euH37FjZv/gY+/9/efYdFcfQBHP9e4eggCEpXUAF7jw3EXsHeYom9GzUm9pgYE0sSYxcLKJaoEWvsJWqssesbe0GMip0iIPXK+wfh4nl0URDm8zx5Hm52Znd23Nz9dnZ2plhxnYVI1q4NYvv2zXTt2h1jYxOd83N0dNIOBQoKCmDVqkA2btyOnZ09iYkJfPnlSCIjI/n88y8IDdW9UUx9yfDUqRPs27ebunW9KFasODEx0ezfv5fz58/qzW/8sRGBsZDnEl8n8+T2q/9WnUt6a9W5UhY4eBTB3qMIxuZZv3Mt7FTPnhK/dhUJe3bqBsT/kpUqjUEmj0UFoSDo128QJ08eZ9GiuURFRWJpWYRSpcowf/4SnaEBqQHYypUBSKXSf1dFK8OSJQuzfKzSpVOmZgsNvac3B3BGmjRpjr//Au0Ubm9ycnImIGA1AQFLmDNnFvHx8RQtakPlylUpVaqMNt/Eid8wd+5PLFmyAIVCQYsWvlSqVJVlyxbp7E+lUqFSqcgKV1c3nJ1dcHR0olix4lk+n3dlbm7O/PlLmDfvZyZPHouRkdG/S0LrrqWgVqvTPJfDhw+iUqnSHUZx8eJ5lEolly9fZMiQvjrbWrb0zfKiG4MGDeeXX2YxefJ44uJev/d5jF1cSnD06GEOHTpAXNxrihSxok6degwcOExn/PCZMylLX2/cuJ6NG9fr7OPNOqa2X+oMGREREdy9exuAiRO/0jt+6nzejo5OJCcnsXTpIm2vfqlSZVi4cBlVq1bP/RP/gCQajUZ/YEohEhn5GqXy3Vc+KizkcilWVqbv1G4ajYaYlwnaWST0Vp0zN8DBI2VKtWJuFsgNPu4hErnRZtmhevrkv4A4jflEZWXcMenTH4WXD5JMXoDJSx+63QqC3Gyz5OQkwsOfULSofY5eCPrYpI77FHSFhT2iW7f2fP/9LBo0aKyzTbRZzoh205fR9421tekHXUZd9BgLH0Rmq84VsTNJGSLhWQQre5NCv+pcTmgSEohdMIfEPTshjR4UWRkPTPoOQOFVXwxBEQQhQ69eRfHw4QNWrgzAzs7+vb+YJgj5hQiMhfcmKUHJ0zv/DpFIY9W5Yq7mOHhaYe9hKVadyw2Ghqhu39QLiuUenhj3HYiirpcIiAVByJKTJ48zc+Y0nJycmTJlmnZuX0Eo6MSVLuSq2MjElF7hm1G8uP/WqnMmcuzdLXHwSJlSzcBQTAWWmyQSCcZ9BhDz77gwedlymPQZgEGdeiIgFgQhW1q18nuvY2UFIb8SgbHwTlJXnQu7GcWTm1G8ep7OqnOe/646J4ZIvDPVwwfEb1yHyZDPkZqZ6WxT1PPGsLUfhg2bYPBJbREQC4IgCEI2iMBYyLbkRBUPr0Xw6HokT25nsOqcZxHMi4pV53KL6sE/xK1eSeIf+0GtRmpTDJM+/XXySCQSzCdMyaMaCoIgCMLHTQTGQpbERSeljBW+/YpnIdGo3nijVqw6934p/7lP/OqVJB46AG/MSRofvAGjzl2RmpplUFoQBEEQhKwSEYyQJo1GQ9STOO0sEpGP31p1zspQu9CGTQkzserce6AMvUfc6pUkHT4IacyqKHNzQxMVBSIwFgRBEIRcIQJjQUulVPM8NIbHNyN5fDOK+Og3FoWQQFEnU5zKWVG2lgMSI1CpCvUU2O+N8l4IcatXkHTkUJoBsbxKNUz6DsCganUxhlgQBEEQcpEIjAu5hNfJPLn1ise3oniW3qpz//YMG5kZ6CwgACIwzk3K0BDiVgaQ9OfhNLcbVKuOSZ+BGFSt9oFrJgiCIAiFgwiMCxmNRkPMi4SUWSRuRfHyYaxOfFvQVp37mCivX08zKDao8QkmffpjULlqHtRKEARBEAoPERgXAmqVmpf/xPL4Vsr8wrERb606Z2+iDYbFqnN5x7B5S+JWr0D95DEABjVrpcxDXKlyHtdMED5+Bw7sZdOmDTx48A8aDdja2lKxYmUGDx6OlZX1B6vHiBGDMDEx4aef5n2Q461fvxZ///mcOHH+gxzvQ1i/fi1btwYTERGOm1tphg0bSbVqNTIsc/PmDZYtW0RIyF1iYqKxsrKmZs1aDBw4FBsb2zTLPH/+jB49OhEfH8+uXX9QpEgRvTydOvmxefNOnbQ7d25x7Nif9OjRGyOjDzczk0qlYuPGdezcuZ2nT59gYWGJl1d9xo6dBMDr17H89ts6Tp8+xcOH/yCXy3F3L8vgwcPx8PDM0jH+97/LrFixlBs3riGVSnFzK83Eid/g4lJCm+fkyeOsWLGU0NB7WFlZ07p1G/r0GYBM9nGsXSAC4wIqKf6/Veee3HlFcsJbq865WfwbDFtiYilWnfuQlLduoo6MQFG7rk66RC7H5LO+JB45lDKGuEKlPKqhIBQsa9euYvnyxXTp0p3+/Yeg0WgIDQ3hwIF9vHz54oMGxl9+OQGZTDyJy6n169eyfPliBg1KCeZ27NjGV1+NIiBgNaVKlU63XGxsDCVKuOLn1x4rKyvCwh6xalUgN29eJyBgDQqFQq/MokXzMDY2IT5ed37+LVuCqVvXC3t7B23a3bt3uHv3Ni1atObOndsEBQXQsWPXDxoYz5r1PWfPnqZPnwGULOlKREQ4N2/e0G5/9uwpO3ZspVWrNgwYMASlUsmmTRsYOrQfS5aszDQ4PnfuNOPGfYGfXzt69eqLUqnk+vWrJCb+19l29eoVJk78ksaNmzF48Aju37/H8uX+xMfHM2LE6Pd16rlKBMYFSGxEgnYWiRf3Y3VWnTM0kWPvUQQHD0uKlxarzuWF5BvXiQ5cTvKpE0iLFcNqw1Ykb30ZG7Zug5Fv2zyqoSAUTFu2bKRlS18+//wLbVqdOvXo3v0z1G9MgZhTiYkJGBpmLQBydXV75+MVVklJSaxZs4IuXT6le/deAFSpUo3evbuxZs0KvvtuZrpla9T4hBo1PtF+rlq1OsWLF+eLL0Zw69YNKlbUfTJ34cI5zp8/S69efVm8eJ7ONkdHJ779dhLe3j4kJyezcOFc7ty5xeDBI975HJ88eUznzm3YtGmHTuCdmbNnT3PgwF5Wrdqgc401btxM+7e9vSMbN/6uE6zXqPEJXbq0ZcuWjUya9G26+1cqlcya9QPduvVk8ODh2vQ6derp5Fu5cjmlS7vz7bc/AFCrVh1UKjXLly+me/deWFsXzfI55RVx2/oRU6s1vHwQy98HHrJv4VX2zL3C5b0PeX4vZSlmC1sjPL3taDTAE7/xVfikgytO5a1FUPyBJV27yoPBgwnv9xnJp04AoH7+nITdO/TyilkmBCH3xcbGULSoTZrbpNL/fga9vGqwfv1ane3r16/Fy+u/x/QXL57Hy6sGp06d4Ouvx9GsmQ9Tpkxg+vSpfPZZV739nzhxDC+vGoSG3gNShlKMGzdaZ19v9upByiNxP79m+PvP16bdvx/KhAljaN7chyZNvBg7dhRhYY90yr1+Hcv3339D06b18fVtgr//fFQqFdmlVCpp06Y5y5f7622bOnUy/fr1yPY+c8PVq38TGxtLkybNtWkymYxGjZry11+n0KQxi09GLCyKACnn+yalUsncuT/Rv/8gLC0t9crVrl0Xf/9Arlz5H+HhL0lIiGfBgqWUL1+BPXt2MmPGdwD4+jbBy6sGnTq9/6W1d+36nerVa2Z442VsbKzXg21oaEjJkq68fPkiw/2fO3eGZ8+e0rFjlwzz3blzi1q16uik1a5dB6VSyZkzf2VyFvmD6DH+yCQnqngWEs3jWykvzyW+1l11zraEOfb/ziIhVp3LW8nXrhK3KpDk06fS3K4KufuBayQI706l0XAnLpoE1bv3tOaEkUxKGRMLZNm4ifTwKMvvv2/FwcGRunW90g2Ss+Pnn2fQrFlLZszohFQqRalUsnfvLu7du4ub23+P9A8dOkCpUmXSDFiqVKmGrW0x/vhjP56eZbXpFy+eIzIyQhsAhoU9YsiQfri5lWLSpKlIpRLWrFnJqFFDWb9+i3YYwMyZ0zhz5jRDhozAwcGBrVs3cffuHb3jdurkh52dPYsWLU/z3ORyOS1b+rJ37y4GDBiivXmIiYnh2LE/GT58VIZt83agmRapVKpzU5IV9++HAlCiREmd9JIl3YiLe82LF88pVqx4hvtQqVSo1WoePw7D338B7u6eer3FwcEbkEqltGvXif379+jt49y5M6xYsYw6depx69ZNjIyMGDVqGEOHjqBOHS969+7P6tUr+OWXhZiamqFQGGTrPHPi2rUreHnVZ9682ezdu5PkZCU1atRk9OixODg4plsuPj6e27dv0aJF6wz3f/36VSwtLbl+/Rr+/gt48iQMJydn+vUbpNMrnZSUiFyuG1oaGKRcn//8cz/nJ/gBicD4IxD3Kkn74tzz0GjUyv/uig2MZNi/seqcwlj8k+a15Kt/ExcUSPLZ02luV3jVx6TPAORZfNlBEPKLZLWaQdfOcO31qzytR3lTS5aXr4VBFgOrL78cz6RJY/nxx5THu/b2jtSr503Xrt2z9bj6TV5ePgwd+rn2s0qlwsrKmoMH9zN4cEpgnJiYwIkTx/jss35p7kMqldKoUVMOHz7I8OGjtE+MDh7cj4tLCdzdU74jgoICMDe3YO7cxRgaprwTUqFCZbp0acOuXb/ToUNn7t8P5ejRI4wf/zW+/w7HqlmzNl27tsvR+fn5tWP9+jWcOfOX9nH5wYP7AGjatEW65VKHAmSmb9+B9O8/OFt1iomJRqFQ6A1bMTc3ByA6OjrTwHjEiEFcufI/ADw9yzF79nydQO7lyxesWhXIjBk/p/uy2IMH9/n22x+wt3dg587tfP75GO7cuc3du7fx9CyHo6MTkHJDltYLe2/SaDQ6vfqpQ3vUarXODYZMJsvwiWJERDh79uzCza0UU6dOJz4+gWXLFjF27ChWr/5NL1hNFRDgT2JiQqY9weHhL4mPT2DWrO8ZOHAozs4u7N27k2+/nYSNTTEqV64CgLNzCW7cuKZT9tq1KwBER+ft90ZWiSgqH0pddS51SrX0Vp1z9ExZdU4qXuTIF5L/vpwSEJ8/m+Z2Q5+GGPfuj7yM+weumSDkjseJcXkeFANce/2Kx4lxlDDO2qqPbm6lWbs2mPPnz3D27BkuX77A5s2/sWfPThYvXk6ZMh7ZrsPbYytlMhkNGzbm8OGD2jGYJ04cJyEhnqZNm6e1CwCaNm3Oxo3r+Pvvy1SuXJXk5GSOHfuTrl27a/OcO3eaxo2bIZPJtMGSubk5pUuX4ebN6wDcuHENjUZD/foNteXkcjne3j5s3rxR55hvz6KQFicnZ6pWrc7u3Tu057p79w58fBpiYWGRbjkbG1sCA9dkuv/0ZoIA/WBRIpG8EaSmFRxqtPkyM3HiFGJiYgkLe8jatUF88cVw/P0DMf13BdFFi+ZRs6bueOS3deyoP2SmTBl3yuTgu/3SpQuMHDlEL/3tG5pJk76lVav0h2RoNBo0GjU//jhXG4w7OjrRv39Pjh49QuPGTfXKHDiwj+DgDYwZMx4nJ+cM66nRaEhKSmTEiNG0b98JgOrVa3L37l3Wrl1J5coLAOjQoTMzZnxHcPAGWrRoRWhoKMuX+yOTybL9hCCviMA4n1Alq3keGq19eS6tVeccPIrgUNYKC1sjMRY1n9EkJhL99QQ0kRF62wwbNsJh9EjiizmhVObN42dByA0OhiaUN7XM8+C4vKklDoYm2SpjYGBAnTpe1KnjBcCZM38xbtxogoJSegezy8rKSi+tadMWbN26ievXr1KuXAX++GM/FSpUxM7OPt39eHqWw8nJhYMH91O5clVOnz5JbGyMzjjaqKgogoM3EBy8Qa+8QpHSe/ry5Uvkcrle0PouM274+bVjxozviIqK4uXLF9y6dYNhw0ZmWMbAwIDSpTMPEDMKkt4OFqtUqcaiRcsxN7cgKSmRxMREbc85QExMLPBfz3FGXFxKAlC+fAWqV69Jx46+/P77Nrp378XVq3/z55+HWL58FTExMQAkJCQAKeO3jYyM9MboZuUmIyOenmV1biRevnzJhAljmDVrDjY2/w35yezJhrm5Bfb2Djo91B4enpiZmXH//j29/OfOnWbmzO/o3r0XHTp0zrSe5uYp11X16jW1aRKJhGrVanDs2BFtWsuWvoSE3GXx4nksWPALBgYG9O07kODgDR/Fi3cgAuM8lRCbzJPbKUMknoVE6606Z1c6ZdU5e/eUVeeE/EtiaIjxpz2J81/wb4IERcPGmPTuj5F7GYysTImPfJ23lRSEd2QglRJYofZHN8Y4LbVq1aFUqTL880+oNk2hUKBUJuvki4mJTrN8Wp0TFSpUws7OnkOHDlCiREnOnDmV6XhcSOk13rZtM6NHf8UffxzAw6Mszs4u2u0WFpbUqVMvzQDGxCTlBsHGxgalUkl0dLROcByZxs16Vvn4NGLevJ85cGAPjx8/xt7eMdP5gnNjKMXbwWLqOZYs6QrAP/+EaoeZANy/fw8TE1NsbYtletw3FS1qg42NLWFhDwF48OAflEol/fr11MvbtWs7GjdumuHMFzlhYmKKp2c57ecn/85jX6pU6WwN8ylRoiRJSUlpbnv7Wr1+/SqTJo2jYcMmDB2a8Y1OqtS2f5tGo9G5yZFIJHz++Rf07TuQp0+fULy4HUqlkuXL/SlfvmIWzyZvicD4A9JoNEQ/T/h3vHAk4Y9e66w6Z2xukLL8smcRirlaIBOrzuU7Go0G5ZX/IS9fEclb48+M23Uk/rdfMahSHZM+/ZC7lsqjWgrC+yOTSPA01X9TPz+LiAjX661KTEzg+fNnOi/F2doW0wmUAc6fP5Pl40gkEho3bsb+/XtwdS2FSqWiUSP9R9hva9KkOUFBAZw8eZxTp47rBYw1anxCaGgIZcp4pDvu1dOzHBKJhGPHjmjHGCuVSo4fP5rl+r9NoVDQvHkrduzYTmRkOJ07f5rp08rcGErxdrCYqkKFSpiZmXHo0EFtYKxSqTh8+A/q1Kmb7SepT58+4cWL59qX02rVqsOCBUt18pw58xfr1q1m5szZODm5pLUbPXJ5SkdWUlJiJjlzT9263qxYsZTIyEjt04ybN68TGxur04N//34oY8eOomLFykya9G2W2+yTT+ogk8k4f/6M9uVHjUbDpUvnKV26jF5+MzMzbXpg4FLs7R0yHJ6Sn4jA+D1Tq9S8+Cc2ZYjEzSheR6ax6ty/44WL2JuIIRL5lEajIfnCOeJWBaL832XMp07H8K0xWxJjY6zWb0ZqmrVxj4IgfBiffdaNevW8+eSTOtjY2PDy5Qs2b97Iq1dRdO78qTZfgwaN2bRpA2XLlsfZuQT79u0mPDw8W8dq2rQF69atJjBwKdWr18zSUIYSJUri7u7B3Lk/kZCQoPOWP0D//oMZMOAzxoz5nDZt2mNtbU1ERDiXLl2kcuUqNG3aAldXN7y9G7BgwRySkpKwt7dn69ZNac7T3LVrO+zs7Jk/f0mmdfPza6+dpSGjMa6pDAwM0gxqc4NCoeCzz/qzfPliihSxwsPDk507t/P4cRjffTdDm+/p0yd07dqOPn0G0LfvQCBlFhFLyyJ4epbDzMyMBw/us2HDr1hbF9XeSBQtaqM3Y8nTp08AqFixSqYv0qUqWbIkAFu3bsLbuwFGRkYZLj6SG9q2bc+WLRsZN240vXv3IyEhgWXL/HF398TLqz6Q8vRgzJgRyOVyunfvpTNNoEJhoNML7+NTixYtWjNx4jdAyhOJDh26sHTpYjQaDc7OJdizZyehofeYNGmqttz161e5fPkiZcq4k5iYyIkTx9i/fw+zZy8QK98VZqmrzoXdjOLp26vOySUUc00ZIuHgUQQTS/3VdoT8Q6PRkHzuDHGrVqD8901mgLjVK1A0bIzkrXFyIigWhPynX79BnDx5nEWL5hIVFYmlZRFKlSrD/PlLdIYG9OkzgMjICFauDEAqldKmTXtKly7DkiULs3ys0qVTpmYLDb3HoEHDslyuSZPm+Psv0E7h9iYnJ2cCAlYTELCEOXNmER8fT9GiNlSuXJVSpf7rrZs48Rvmzv2JJUsWoFAoaNHCl0qVqrJs2SKd/alUqizPb+zq6oazswuOjk6ZzvjwIXz6aU9Aw+bNvxEZGYGbW2lmz56vE3imvrz35k1B2bLl2bFjG1u3biI5OYnixe2oU8eLzz7ri6VlkVyto7u7J/36DWLXrt9Zv34NxYoVf+exyJkxNTVjwYKlzJ8/m++++xqpVErt2nX5/PMvtUMdQkPv8fz5MwBGj9a9Nu3s7HXq+Hb7AQwfPgoTExN+/XU1r15F4erqxo8/ztVZMc/AwIA//zxMUFAgAOXKVWDhwmVU+IhWcpVosjsjdgETGfk6V16Iio1ISJlF4mYUL/6JQfPGLg1NU1edK0LxUhYf9QIbcrkUKyvTXGu3/Eqj0ZB89jRxQQEor13VzyCVYrlkBQblyme6r8LSZrlNtFv25WabJScnER7+hKJF7bXzkBZkcrlUXGdpCAt7RLdu7fn++1k0aNBYZ5tos5wR7aYvo+8ba2vTD7qMuugxziG1WkPEo/+GSES/SNDZbmFr9O94YSusnUyRSsUQiY+BRqMh+fSplB7i62kExDIZhs1aYvJZX2SZTG8jCILwsXr1KoqHDx+wcmUAdnb2eHn55HWVBOGDEIFxNmhXnft3fuHEOP1V51JfnjOzFqvOfUw0Gg3Jp04Qt3oFyhvX9TPIZBi2aIVJr77I/p28XRAEoaA6efI4M2dOw8nJmSlTpqW7QIQgFDTiSs+EWHWucEjYvJHXC+bob5DJMGzpi0mvPsgyWFZTEAShIGnVyi9LL9sJQkEjIrm3aDQaIh/HaRfaiHqiu+qcmbWh9sU5sepcwWHYtAWvA5ZAfHxKglyOUSs/jHv2RpbDJWMFQRAEQfi4iMCY/1adS315Lj7mrVXnnM1w8EiZUs1crDr3UdOo1WhevUL61qpV0iJFMO7QmfiN6zFq3SYlIM5gtSpBEARBEAqeQh0YJyUoObb2Nk/uvEKV/N8bonKFlOKlLHDwtMLe3VKsOlcAaNRqko4eIW7VCiSmplguXq53g2PcvRdG7TshK26XR7UUBEEQBCEvFerAOD4mmUfXIwEwtjDQDpEQq84VHBq1mqQ/DxG3eiWqeyHa9OSL51G8seY7gNTCEiw+rhW9BEEQBEHIPYU6MJYrpFRs4ohdGUux6lwBo1GpSDpyiLjVK1DdD9XbHr9ujV5gLAiCIAhC4VaoA2NTS0MqNnYSE20XICkB8R/ErVqJ6h/9gBiFIUZt22PS/bMPXzlBEARBEPK1Qh0YCwWHRqkk8dBB4tesRPXgH/0MhoYYteuIyac9kRa1+fAVFAQhz6xYsYygoAAAJBIJJiYm2NnZU7lyVTp06ELJkq46+UeMGISJiQk//TTvnY+9fv1a/P3nc+LE+Xfe14f06NFDNmxYy7VrVwkNDcHFpQRr1wZnqay//wJOnTrBs2dPkUgkuLiUoFu3HjRp0lwn35Mnj1m6dBGXL18kLu41zs4udOvWk2bNWurtc8WKZdjbO+hNIRccvB5nZxfq1PHK+cm+g3nzZrN582906NCZMWPG620/fvxP1q5dRUjIHRQKQzw8PJk+/UdMTc3T3eeGDb9y4MAenjx5jFKpxMHBkbZtO9ChQxe9J9vPnj1l6dJFnD37FwkJCTg4ODJw4DDq12+QuydaiIjAWPjoaZRKovr1RBV6T3+jkRHG7Tph/GkPpNZFP3zlBEHIFwwNDZk/fykA8fGvCQm5y44d29i5czsTJkyhdWtfbd4vv5zwQZegzY9CQ0P466+TlCtXHo1GjVqd9SerCQnxtGvXAReXkmg0Gv788xBTp05GrdbQrFkLABITE/niixFIJDBy5BgsLCz544/9TJs2BUNDQ3x8GvHw4QMuXbqAn1877b6VSiXBwRvw9W2LhYUFwcEbqFvXK08C45CQu+zevQNTU9M0t+/Zs5PZs2fSrVtPhgwZQVxcHJcuXUCpTE4zf6rXr2Np2rQFrq5uyOUGXLhwjnnzZvP69Ws++6yfNt+LF88ZMqQfrq5uTJgwBRMTU0JDQ0hKSszV8yxsRGAsfPQkcjnyCpV0A2NjY4zbd8K4Ww+kVtZ5VzlBEPIFqVRKhQoVtZ9r1qxN+/adGTduNLNmfU+VKlUoXjxlznJXV7e8qma+Ua9efby9GwAwffpUbt5MY0XQdLzdc1qrVh3u37/H3r07tYHxzZs3ePToAQsWLKVatRoA1KxZi2vXrnLo0EF8fBphaWnJ/fv3GDlyCFZW1oSHv2T79i3UreuFQqF453OcPn0qAJMnT81R+blzf6Jbtx7s3btLb9urV1HMmzebUaO+om3bDtp0L6/6yOXSDIdwDhgwROdzzZq1ePbsKXv27NIJjBcvno+9vQOzZy9AKk25kasu3p15Z4X7llj46GhUqjTTTXr1AZkMibEJxj16Yx38O6ZDPxdBsSAI6TI0NGT06LEkJyezY8c2bfqIEYMYN260Tt7790OZMGEMzZv70KSJF2PHjiIs7JFOntevY/n++29o2rQ+vr5N8Pefjyqd76yMKJVK2rRpzvLl/nrbpk6dTL9+PbK9z+xKDbRyi4WFJUqlUvtZpUr529TUTCefmZkZGo1GW2bkyC/p23cgJ04c5c8/DzNx4jf06TMAIyMjOnXy4+nTJ2zdugkvrxp4edVgz56duVrv9Bw4sJfHj8Po0aN3mtsPH/4DjUaTa6sHWlpaatsMIDY2lj//PET79p1y/d+qsBOtKXwUNMnJJOzcTmT3Tihv3dTbLrN3wPy7GVht2o7pkOFIixT58JUUBOGj4+rqhq1tMa5cuZJunrCwRwwZ0o/o6GgmTZrKt9/+QFRUJKNGDSUpKUmbb+bMaRw79idDhoxg8uSphIbeY+tW/XG5nTr5MWLEoHSPJ5fLadnSl717d+kMYYiJieHYsT9p3bpthuekVCoz/S87QyNyQqPRoFQqiYmJYd++3Zw7d4YOHTprt1eqVIWSJd1YtmwxYWGPiI2N5ffft3Lz5nXateuoPd/Fi+cTFBSAl5cPPj6NmDlzGmvXBpGYmMCMGT9TtGhRGjRozNKlQSxdGvRBhlTExb1m8eL5DB8+CiMjozTzXLt2BReXEuzZs5MOHVrj41OLfv16cuHCuSwfR6lUEhcXx6lTJ9i3bzedOnXVbrt9+yZKpRKpVMqwYQPw8alF27bNCQxcmqObMeE/YiiFkK9pkpNJ2LOT+F9Xo376BIC4VYFYzJytl9fQp+GHrp4gFDoajUZnQaQPTWYgzfWpNYsVK054+Mt0twcFBWBubsHcuYsxNDQEoEKFynTp0oZdu36nQ4fO3L8fytGjRxg//mt8fVMC15o1a9O1a7sc1cnPrx3r16/hzJm/qFOnHgAHD+4DoGnTFumWe/LkMZ07t8l0/337DqR//8E5qltWnD9/li++GA6ATCbjiy/G0bBhE+12uVzOwoVLGT9+jLaNDAwMmDx5qnY4QGRkBE5OzgwbNpKVK5djb+/Al1+OJzh4A4mJibi7e2JgoMDa2lpnmEx6VCqVtjca0P79Zk+2RCJBJpNluJ8VK5bj5ORM48bN0s0TERHOgwf/sHLlcoYO/RwrK2s2blzP2LGj2LBhs3bYTnoePXpIt27ttZ979+5P167/PSlIvV5/+mk6bdt2YMCAIfz992WCggIwMDCgd+/+Ge5fSJ8IjIV8SZOU9F9A/OypzrakE8dQ3rmNvIx7HtVOEAonjUbD4cCbhD+IzbM62LiY0XCAZ64GxxqNJsP9nTt3msaNmyGTybRBlLm5OaVLl9GOvb1x4xoajYb69f+7QZfL5Xh7+7B580ad/W3enPnjficnZ6pWrc7u3Tu0gfHu3Tvw8WmIhYVFuuVsbGwJDFyT6f5tbGwzzfMuypevQGDgGmJjYzl9+hRz5/6EXC7D17cdAImJCXz99XjUajXTp/+MmZkZJ04cY+bMaZibW1C7dl1cXErg4lJCZ79yuZzu3XvlqE6jRg3l8uWLeun79u3W/m1nZ5/hv09o6D22bdvEsmVBGR5LrVYTHx/HtGkztL3YVapUpXPntqxbtzbNGSzeVKxYcQID1xAXF8f//neJX39dhVQq1d7MpAb1n3xSh2HDRgFQrVoNoqIi+fXX1fTq1VcMscghERgL+YomMfHfgHgV6ufP9bZLzMwx7tINqZ19HtROEISCuAzSixfPcXFxSXd7VFQUwcEbCA7eoLdNoUh5lP7y5Uvkcrle0Gr1Du85+Pm1Y8aM74iKiuLlyxfcunWDYcNGZljGwMCA0qUz7zR430GTiYkpnp7lAKhR4xOSk5NYuHAuLVv6IZPJ2LXrd65fv8rWrXuwsrICUl4ce/r0CUuWLKB27bo6+8uN3u1x4yYRFxen/bxyZcoUfv36DdSmGRhk/FLfwoVzadiwMXZ2DsTExAApQXBycsqwEVNTU6RSKRb/rqKa+mIhgKGhEeXLVyA0NCTNfb9JoVBo269atRoYGRmzdOlC2rXrSNGiNpibp1xn1avX0ClXrVpNNm/eyNOnT3BwcMz0OII+ERgL+YImMZGEXb8Tv24N6hdpBMTmFhh3+RSjTl2RmpmlsQdBEN43iURCwwGeBWooxb17Ibx48ZzWrdN/ScrCwpI6derpjJFNZWJiAoCNjQ1KpZLo6Gid4DgyMiLHdfPxacS8eT9z4MAeHj9+jL29o06glZb8MpTibR4eZdmyJZioqEiKFrUhNDQUG5ti2qA4VZky7pw7d/q91MHFpaTOZ0vLlOA1NQDNigcP7nP27BP279+rk75z5zZ27tzGunWbKVGiJCVKlEzzOk15OpH9mxIPD09UKhVPnjyhaFEbvbm339w/vP8bn4IszwPj48ePs2zZMu7evUtsbCzFixenSZMmjBgxAnPz/ybAPnr0KHPnziUkJAQ7Ozv69OlDjx7v/81c4f1LOn2K2Fk/oE5jjJ/EwgLjrt0x6tgFqakIiAUhr0kkEuSKjMdgfiwSExOZN+9nFAoFbdq0TzdfjRqfEBoaQpkyHumOP/X0LIdEIuHYsSPaMcZKpZLjx4/muH4KhYLmzVuxY8d2IiPD6dz500xvCvLLUIq3/f33ZUxNTbG0LAKAnZ0dL18+JzIyQqdX/ebNG9jZZTz+9k1yuYHOC5Dv29SpM/TmCZ46dTLly1ekc+duFC9uB0C9et4EBQVw/vw56tXzBiAhIYGrV6/QvHn6Y8TT8/ffl5FIJDg4pLSNvb0Drq5unD9/lnbtOmnzXbhwFnNzC209hOzL88D41atXVK1ald69e2NhYcGdO3dYuHAhd+7cYeXKlQBcunSJYcOG0bZtWyZMmMDFixf54YcfUCgUdO6sfwcvfFykNrZ6QbHE0hLjLt0x6tQFqUnak6cLgiBklVqt5urVlJkn4uPjuHcvZYGPx4/DmDTpWxwcHNKdW7Z//8EMGPAZY8Z8Tps27bG2tiYiIpxLly5SuXIV7WIM3t4NWLBgDklJSdjb27N166Y0Z3/o2rUddnb2zJ+/JNN6+/m1Jzh4A1KpNEtTfxkYGGSrBzQ9CQkJ/PXXCQCePn3C69evOXLkDwCqVKmu7emdOXMa+/bt5ujRMwDcvXuHJUsW0rBhY+ztHYiPj+PkyePs2vU7Q4aMQC5PCTuaNWvJr7+u4ssvR9KzZx/Mzc04evRPTp06zldfTchyPUuWLMmFC+c5d+405uYW2Ns7aIPv9yGtl/wUCgW2trY6vfmenuXw9vbhxx9/YMiQEVhZWbFx43oSExN0pngLCgpg1apANm7cjp2dPbGxsYwdO5JmzVrh5OSEUqnkwoXzbN78G23bdsD6jYWqBgwYytdfj2PhwjnUrl2Pv/++zO+/b2X48NG5/oJqYZLngbGvry++vv+tOFSrVi0UCgVTpkzh2bNnFC9enMWLF1OuXDlmzJgBQO3atXny5Anz58+nY8eO4pHBR05eugyK+g1IOvYnEssiGH/aA6P2nURALAhCrklMTGTIkL5IJBKMjU2ws7OjevWazJgxmxIlSmZY1snJmYCA1QQELGHOnFnEx8dTtKgNlStXpVSpMtp8Eyd+w9y5P7FkyQIUCgUtWvhSqVJVli1bpLM/lUqV5Sm1XF3dcHZ2wdHRiWLFimf7vHMqMjKCKVN0A9TUzwsWLMXKKiUIVKvVOudibW2NmZkZq1YFEhERjqmpGSVKlGTmzNnaBUMg5eWyhQuXERCwhHnzfiY+Pg4nJ2cmTPg60+no3jRo0HB++WUWkyePJy7uNZMmfZtrcwe/qylTvmfJkoX4+88nLi6OsmXLM2/eEooVK6a9CUttv9QhEAqFAmfnEmzcuI4XL55jaGiEo6MTY8dOokWL1jr79/FpyJQp37NmzUq2bAnG1rYYgwePoHPnbh/8XAsSiebNuUvyiQMHDvD5559z+PDhf+/CqvHVV1/Rp08fbZ6zZ8/Sq1cvtmzZQoUKFXJ8rMjI1xmuQCPoksulWFmZZrvdNPHxxG/fgiYyAtM0Xh5R3r1N0tkzGLfriOTfMXsFRU7brLAT7ZZ9udlmyclJhIc/oWhR+0xfSCoIMluNLK+EhT2iW7f2fP/9LBo0aJzX1dGRX9ssvxPtpi+j7xtra9MPukR7nvcYp1KpVCiVSu7evcvixYtp2LAhjo6O3L17l+TkZNzcdJfoLF26NAAhISHvFBgL75cmLo74bZuJ/20dmqhIkMkwatcR2Vtvy8pLuyPPwpvUgiAIhcGrV1E8fPiAlSsDsLOzx8vLJ6+rJAiFQr4JjBs2bMizZ88A8Pb2Zs6cOUDKGGRAbwqc1M+p23PqQ96FFASp7ZVZu6lfvyZuczCvN/yK5s1/I5WKhF9XYTlpyvusZr6S1TYTdIl2y77cbDO1uvCMUUwdjimRQH55hnry5HFmzpyGk5MzU6ZM047NzS/yY5t9DES7ZUwmkyCX635/fejh0vnm/7Tly5cTFxfH3bt38ff3Z8iQIQQF/TeBdnoDyd91gLmFhfE7lS+s0ms3VWwskb/+SkTQKlRp3LTIbW2xqFIRK6vCN35YXGs5I9ot+3KjzRISZLx8KU3zh6qgyk83YW3atKVNm6yPtc0r+anNPiai3XSp1RKkUimWlibpLrP9oeSbwNjT0xOAatWqUa5cOTp27MjBgwe1Qybe7hmOjo4G9HuSsys6Oh6VSoz1ySqZTIqFhbFeu6ljY4nb9BuvN6xHExOtV05qY4vpZ70xadMeiaEhkZGvP2S181R6bSZkTLRb9uVmmyUlJf77YpCmwI+HlEhS2k6lUotevCwSbZYzot3SplJpUKvVvHoVR3y87ouplpbGH3SShXwTGL+pbNmyyGQyHjx4QKNGjTAwMODevXvUr19fm+fu3bsAlCpV6p2OpVKpC/yX/vuQ2m4alYr4NUHEB29AExujl09qWwzjnr0xat0GiaEhKoBC2t7iWssZ0W7ZlxttplIVnl/t1ABFBCpZJ9osZ0S7ZSytG/EP3Vb5si//0qVLqFQqnJycUCgU1K5dm717dVeZ2bVrF7a2tpQr9+7zNQo5J5HJSLpwTi8olhYrjumYcVj9thXjDp2RGBrmUQ0FQRAEQRCyJs97jEeMGEGFChXw8PDAyMiImzdvEhgYiIeHB02aNAFg+PDh9OzZk6+//ho/Pz8uXrzIpk2bmDZtmpjDOB8w6TuA6NHDAZAWt8O4Vx+MWvoiURT8KZ4EQRAEQSg48jwwrlSpEnv27GH58uVoNBocHR3p0qUL/fv3R/FvYFW1alX8/f2ZM2cO27dvx87Ojq+//lqsevcBqV9FEb/pN8y6dIO3XpwzqFYDw6YtMKhSDcOWrZEYGORRLQVBEARBEHIuXy7w8SGJxQMypo6KIv63dSRs3YQmPg7THr1wmTJJtFs2iIUqcka0W/aJBT5yTiy6kH2izXJGtJs+scCHkO+pIyOJ37iO+K2bID5emx63ZRPK4UNAIsYMC4IgCIJQsIjAWNChjowgfsOvxG/bDAkJetultsVIfvwEHEt++MoJgiDkwIoVywgKCgBS5r43MTHBzs6eypWr0qFDF0qWdNXJP2LEIExMTPjpp3nvfOz169fi7z+fEyfOv/O+PqTDh//g4MG93Lp1k+joVzg6OtGuXSfatu2Q4bs9r1/H8ttv6zh9+hQPH/6DXC7H3b0sgwcPx8PDUy//vXshLFu2iMuXL6JWq3FxKcno0V9RsWJlnXwrVizD3t6BVq38dNKDg9fj7OxCnTpeuXPi2TRv3mw2b/6NDh06M2bMeG36xYvnGTlyiF7+xo2bMn36jxnuU6PRsH79GrZv38LLly9wcnKmT58BNG7cTCdfVFQUAQH+nD59iqioSOztHWjTpgOdO3d75zUeCjMRGAsAqCPCUwLi7VvSDIhlzi4Yf9YP0xYtMLa1JKEQzUMsCMLHz9DQkPnzlwIQH/+akJC77NixjZ07tzNhwhRat/bV5v3yywmFfgGG3377FTs7e4YNG4m1dVEuXjzPvHk/8/hxGMOHj0q33LNnT9mxYyutWrVhwIAhKJVKNm3awNCh/ViyZKVOcHz37h2GDx9AnTpeTJ06A7lcxq1bN0n49zfo4cMHXLp0AT+/dtoySqWS4OAN+Pq2xcLCguDgDdSt65UngXFIyF12796BqWn6C1ZNmvQtLi4ltZ+LFCmS6X7Xr1/D8uX+9O7dn4oVK3H8+FGmTp2MoaERXl7/TVs7efJYHj16wKBBw7Gzs+fcuTMsWPALarWKbt16vsupFWoiMC7k1OEviVu/loTft0Jiot52mUsJjHv3x7BxUyQyGZJCsgKWIAgFi1QqpUKFitrPNWvWpn37zowbN5pZs76nSpUqFC/uAICrq1teVTPf+PHHuVhZWWk/V6tWg/j4OLZsCWbgwKHal+PfZm/vyMaNv+usXlajxid06dKWLVs2MmnSt9r02bNn/hsUT9em1axZW/u3paUl9+/fY+TIIVhZWRMe/pLt27dQt65XusfPjunTpwIwefLUHJWfO/cnunXrwd69u9LN4+ZWCk/PrE8rm5yczOrVK+nUqRv9+g0CUtrk6dMnBAQs0QbGz58/43//u8TEid/QunUbAKpXr8ndu7c5dOigCIzfgYhyCrnkK3+TELxBLyiWlXDF/NvvKbLmN4yatUAik+VRDQVBEN4PQ0NDRo8eS3JyMjt2bNOmjxgxiHHjRuvkvX8/lAkTxtC8uQ9NmngxduwowsIe6eR5/TqW77//hqZN6+Pr2wR///moVLqreGWFUqmkTZvmLF/ur7dt6tTJ9OvXI9v7zK43g+JUZcp4kJSUqF15Ni3GxsZ6S/oaGhpSsqQrL1++0Kbdvx/K1at/06lT13T3ZWFhyciRX9K370BOnDjKn38eZuLEb+jTZwBGRkZ06uTH06dP2Lp1E15eNfDyqsGePTtzcLbZd+DAXh4/DqNHj965ut+wsEfExb2mVq06Oum1atUhJOQOT58+BVKuEQAzMzOdfGZm5kChnlPhnYke40JOUb8BMrdSqO6FACAr6YpJnwEoGjQSwbAgCAWeq6sbtrbFuHLlSrp5wsIeMWRIP9zcSjFp0lSkUglr1qxk1KihrF+/Rdt7OXPmNM6cOc2QISNwcHBg69ZN3L17R29/nTr5YWdnz6JFy9M8nlwup2VLX/bu3cWAAUO0Y3pjYmI4duzPDIcywH9BU0akUmm21wH43/8uY2FhmWbQnJH4+Hhu375FixattWnXrqW0d2xsLH36dCc0NARb22J069aDTp26ASnnu2bNSm7evI6Xlw+mpmbMnDkNb28funT5lBkzfmbs2FFUrFhF20Pq6OiUrbrlRFzcaxYvns/IkWP0bgLe9tVXo4iOfkXRojY0adKcAQMGI5ebpJs/KSmlk0ou1w3PUmdq+OefUOzs7HBwcKRmzVqsXr0SZ2eXf4dSnOXYsT8ZO3biO55h4SYC40JC9fwZqn/uo6hZSyddIpVi0qc/catWYNKnPwqfRkjEoimCIGRA9ewp6mdPs1VG5lwC6VsBlSY5GeWNa9naj7S4HbLidtkqk5lixYoTHv4y3e1BQQGYm1swd+5iDP9dxbNChcp06dKGXbt+p0OHzty/H8rRo0cYP/5rfH3bAimPwLt2bZejOvn5tWP9+jWcOfMXderUA+DgwX0ANG3aIt1yT548pnPnNpnuv2/fgfTvPzjL9bl58zp79uygb9+ByLLZaRIQ4E9iYgIdO3bRpkVEhAMwbdoUPv20J+XLj+HEiaPMmzcbCwtLmjVrSWRkBE5OzgwbNpKVK5djb+/Al1+OJzh4A4mJibi7e2JgoMDa2lpnmEx6VCoVb85Qm/r3mzcSEokk0/NbsWI5Tk7Oei/DvcnMzIzu3T+jSpVqGBoacuHCOX777Vf++SeUOXMWpFvOyckZqVTKjRvXqFathjY99UYiOvqVNm3GjNl8++1EPvusm7buQ4eOpGVLX4ScE4FxAad69pT4tatI2LMTiYkJ1sG/IzHRvVtV+DQSAbEgCFmWsHsH8UGB2Spj/u33GDZprpOmefWKV8MHZWs/xn0HYNove2Uyo9FoMnyL/9y50zRu3AyZTKYNoszNzSldugw3b14H4MaNa2g0GurXb6gtJ5fL8fb2YfPmjTr727w588f9Tk7OVK1and27d2gD4927d+Dj0xALC4t0y9nY2BIYuCbT/dvY2GaaJ1V4+EsmTx5HuXLl6dmzT5bLARw4sI/g4A2MGTMeJydnbbpanTKPr69vG3r16gukjGMOC3vEmjUradasJS4uJXBxKaGzP7lcTvfuvbJVh1SjRg3l8uWLeun79u3W/m1nZ5/hv09o6D22bdvEsmVBGR7L3d0Td/f/XjSsXr0mRYvaMHfuT1y7dhUPj7THHZuYmNK8eSvWrVuDm1tpypevyMmTx/jjj/0A2l5+jUbDjBnf8eDBA7755gdsbW25dOkCAQH+WFiY4+vbLsP6CekTgXEBpXr6hPhfV5Gweyf8+0WuefWK+G2bMenxmU5eERALglCYvXjxHBcXl3S3R0VFERy8geDgDXrbFIqUR+kvX75ELpfrBa1WVtY5rpefXztmzPiOqKgoXr58wa1bNxg2bGSGZQwMDChd2j3TfWd1GEVsbCxffTUSIyMjZs2ao/eIPyPnzp1m5szv6N69Fx066K5Ua2FhCUC1ajV10qtXr8np06dQKpU6x8pO73Z6xo2bRFxcnPbzypUpU/j16zdQm5bZYjYLF86lYcPG2Nk5EBMTA6QE+cnJSmJiYjA1NU23bRs1asrcuT9x8+aNdANjgM8//4Lw8HDGjk0ZMlOkSBEGDBjK4sXzsLYuCsCpUyc4cuQPVq3aQOnSZQCoWrU6sbExLFo0n1at2mR7qIyQQgTGBYzqyWPi1q4icc9OSOOlj+SL5+GtwFgQBKGwuncvhBcvntO6tV+6eSwsLKlTp55ecAdg8u8TOBsbG5RKJdHR0TrBcWRkRI7r5uPTiHnzfubAgT08fvwYe3tHncfracnNoRSJiYlMmDCGiIgIli0LwtKySJbrfv36VSZNGkfDhk0YOlQ/mC9RomSa5TLrvX8Xb06bBimzXgDZmjXiwYP7nD37hP379+qk79y5jZ07t7Fu3eZ0zy2rLCwsmTNnIS9fviA6+hVOTi6cOHEMAwMD3N09ALh//x4ymYxSpUrrlC1TxoPY2A28evUq22PBhRQiMC4gVI/DiFsTROK+3WkGxHLPshj3GYCibt5Mgi4IQsFh1LoNihqfZKuMzLmEXprE0hLLxWm/gJYeaS6OL05MTGTevJ9RKBS0adM+3Xw1anxCaGgIZcp4pDv+1NOzHBKJhGPHjmjHGCuVSo4fP5rj+ikUCpo3b8WOHduJjAync+dPMw0ac2sohVKp5JtvJnD37m0WLQrAzs4+y/W+fz/035fiKjNp0rdp1rlixcqYm1tw/vxZateuq02/cOEcJUu6ZblnWi43ICkpKct1e1dTp87QviD3X9pkypevSOfO3SiewfV56FDKcIhy5cpn6Vg2NrbY2NiiUqnYvn0zjRo1xdQ0ZRYKOzt7VCoVd+7c0hmycfPmdYyNTbI0X7KQNhEYf+RUjx6mBMQH9qYdEJcth0nfgRjUritWwhEEIVfIcukFOImBAQaVqrx7hbJArVZz9WrKC0zx8XHcu5eywMfjx2FMmvQtDg4OKJXqNMv27z+YAQM+Y8yYz2nTpj3W1tZERIRz6dJFKleuQtOmLXB1dcPbuwELFswhKSkJe3t7tm7dpB1L+6auXdthZ2fP/PlLMq23n197goM3IJVK9VZ9S4uBgUG2ekDTM2fOj5w8eZxhw0aSkJCgbTsAV1dXbYA2c+Y09u3bzdGjZ4CUHvIxY0ZoxwLfvHlDW06hMNAGcQYGBvTtOwB//wWYmZlRvnwFTp48zqlTJ5gxY3aW61myZEkuXDjPuXOnMTe3wN7eIVs929mV1kt+CoUCW1tbnd78adOm4OjohIeHJwpFyst3wcHr8fb2oWzZctprLSgogFWrAtm4cbv25uPAgb0kJibi6OjEy5cv2bFjK48fh/HNNz9o91+njhf29g5MmTKBvn0HYmNjy4UL59i+fQvduvUUv/fvQATGHzGNWs2rr0ahfmsuTQB5+QopAfEntcX/IIIgFHqJiYkMGdIXiUSCsbEJdnZ2VK9ekxkzZmf66NvJyZmAgNUEBCxhzpxZxMfHU7SoDZUrV6VUqTLafBMnfsPcuT+xZMkCFAoFLVr4UqlSVZYtW6SzP5VKleX5jV1d3XB2dsHR0YlixYpn+7xz6uzZ0wD4++vPoLBgwVJtEKhWq3XOJTT0Hs+fPwNg9OhhOuXefrGtS5fugIRNm35j1apAHB2dmDx5KvXrN8hyPQcNGs4vv8xi8uTxxMW9ZtKkb7N0A/G+ubq6ceDAXn77bR3JyUnY2zvQq1df7YuGqVLb7+3ZMn777VeePHmMsbExtWvX45tvvsfGxkabx8TEhHnz/Fm+3J9lyxYTHf0Ke3tHBg8e/m+7Cjkl0bz5r1EIRUa+TreX4GMQ//tWXs+epf0sr1AJk74DMKhZ670ExHK5FCsr04++3T4k0WY5I9ot+3KzzZKTkwgPf0LRovaZvpBUEMjl0nx5nYWFPaJbt/Z8//0sGjRonNfV0ZFf2yy/E+2mL6PvG2tr0w+6RLvoMf5IKB/8g8zeAYmBgU66USs/4tcEIS1uh0m/gRhUryl6iAVBED5yr15F8fDhA1auTBnf6+Xlk9dVEoRCQQTG+Zwy9B5xq1eSdPggZl9NxKhNO53tEgMDiiwPQmJdVATEgiAIBcTJk8eZOXMaTk7OTJkyLVvTpAmCkHPi/7R8SnkvhLjVK0g6cgj+He0S92sQhq18kbz1BSktapPWLgRBEISPVKtWfvlirKwgFDYiMM5nlCF3iVsVSNKfh/W2qZ88IenMXxjW886DmgmCIAiCIBRsIjDOJ5R3bxO3agVJR4+kud2ges2Ul+oqV/3ANRMEQRAEQSgcRGCcx5R3bqf0EB/7M83tBjVrYdJnAAaVKn/YigmCIAiCIBQyIjDOQ4mHDxLz7eQ0txnUqoNJn/4YVKj0gWslCIIgCIJQOInAOA8Z1KqDxNwCTUz0f2m166UExOUr5GHNBEEQBEEQCh8RGH8gmrg4JCYmOmlSUzOMu35KXOAyDOrUSxkykcU11AVBEARBEITcJQLj9yz52hXiggJRh7+kyIq1SKS6q7cYdeqKQa06GOTC2vaCIAiCIAhCzonA+D1JvvI3cUEBJJ87o01LOnEMw7fWgJeamiEVQbEgCMJ7s2LFMoKCAgCQSCSYmJhgZ2dP5cpV6dChCyVLuurkHzFiECYmJvz007x3Pvb69Wvx95/PiRPn33lfH9KjRw/ZsGEt165dJTQ0BBeXEqxdG5ylsocOHeDw4YNcu3aVly9fMGzYKLp376WXz8urhl6atXVRduzYr5e+YsUy7O0d9OZ2Dg5ej7OzC3XqeGXxzHLXvHmz2bz5Nzp06MyYMeO16dOnT2Xv3l1plhk27HO6d++d7j7TKzt79gJq166r/dypkx9Pnz7Ry3fo0EkMDQ2zcxrCG0RgnMuS/75MXFAgyefP6m2LW70ShbePWKFOEAThAzM0NGT+/KUAxMe/JiTkLjt2bGPnzu1MmDCF1q19tXm//HICMpk0vV0VCqGhIfz110nKlSuPRqNGrVZnueyRI4d4/DiMevW8+f33rRnm7dSpK02atNB+NjAw0P798OEDLl26gJ9fO22aUqkkOHgDvr5tsbCwIDh4A3XreuVJYBwScpfdu3dgamqqt61PnwG0bdtRJ+3w4QMEB2+gTp16me7bwcGRb775QSft7Rs4gAYNGtOtW0+dNIVCkZXqC+kQgXEuSb58MSUgvph2r4CiQSNMevcXQbEgCEIekEqlVKhQUfu5Zs3atG/fmXHjRjNr1vdUqVKF4sUdAHB1dcurauYb9erVx9u7AZDSg3nz5vUsl502bSbSf4cNZhYYFytmp/Pv8iZLS0vu37/HyJFDsLKyJjz8Jdu3b6FuXa9cCf6mT58KwOTJU3NUfu7cn+jWrUeavbuOjk44OjrppC1dupCSJd0oU8YdpTLjGw1DQ8N02+VN1tbWWconZF3hviXOBUmXLvBq5FBefT5EPyiWSFA0akKR1Ruw+H4W8tJl8qaSgiAIgh5DQ0NGjx5LcnIyO3Zs06aPGDGIceNG6+S9fz+UCRPG0Ly5D02aeDF27CjCwh7p5Hn9Opbvv/+Gpk3r4+vbBH//+ahUqmzXS6lU0qZNc5Yv99fbNnXqZPr165HtfWaXVJrz8OBdyr7JwsKSkSO/pG/fgZw4cZQ//zzMxInf0KfPAIyMjLRDCbZu3YSXVw28vGqwZ8/OXDl2Zg4c2Mvjx2H06JH+kIg3vXjxnL//vkyzZi0yzyzkKdFjnEMajYbocV+QfPqU/kaJBEWjppj07ovctdSHr5wgCMJ7JI0JQxoTlq0yKqtSaIyLvpWYhPzZ5WztR23uiNrcMVtlMuLq6oatbTGuXLmSbp6wsEcMGdIPN7dSTJo0FalUwpo1Kxk1aijr12/R9l7OnDmNM2dOM2TICBwcHNi6dRN3797R21+nTn7Y2dmzaNHyNI8nl8tp2dKXvXt3MWDAEG2gGRMTw7FjfzJ8+KgMz0mpVGZ63lKpNNcC2Hexbt0qli1bhLGxMZ98Uodhw0ZhZ2cHpJzvmjUruXnzOl5ePpiamjFz5jS8vX3o0uVTZsz4mbFjR1GxYhXtcIK3e2nfh7i41yxePJ+RI8dgZGSUpTIHD+5HrVbTpEnzLOUPCwujRYsGJCQk4OZWmj59BlD/rXeUAA4c2MfOnduRy+VUrlyVoUNHUqpU6eycjvAWERjnkEQiQVaipG5gLJFg2KQZxp/1Q57GWCBBEISCwOjGb5iem5utMtFNF5Ho3k4nTZoQidW2Dtnaz+uaXxD3yZfZKpOZYsWKEx7+Mt3tQUEBmJtbMHfuYu1LTRUqVKZLlzbs2vU7HTp05v79UI4ePcL48V/j69sWSBmu0bVruxzVyc+vHevXr+HMmb+0Y1IPHtwHQNOm6fc6PnnymM6d22S6/759B9K//+Ac1S23tGjRmrp1vbG2tubevRBWrw5k2LD+rFq1AQsLCyIjI3BycmbYsJGsXLkce3sHvvxyPMHBG0hMTMTd3RMDA0WWhxOoVCo0Go32c+rfb95ISCQSZDJZhvtZsWI5Tk7ONG7cLMvn+scf+6hQoRIODpnf1JUp44GnZzlcXd2IjY1l+/bNTJr0Fd9/P4uGDZto83l51adcuQoUL27H48dhrF69kmHD+rNy5boPcoNQUInAOAs0Gk2aY4NNPu1JwrYtoEzGsHEzjHv3Q16i5IevoCAIgpBj6X3Hpzp37jSNGzdDJpNpgyhzc3NKly6jHXt748Y1NBoN9es31JaTy+V4e/uwefNGnf1t3pz5434nJ2eqVq3O7t07tIHx7t078PFpiIWFRbrlbGxsCQxck+n+bWxsM83zvn399Xfav6tUqUalSlXo378nO3duo0eP3ri4lMDFpYROGblcnuYMF1kxatRQLl++qJe+b99u7d92dvYZ/vuEht5j27ZNLFsWlOXj/vPPfW7fvsUXX4zNUv4uXT7V+ezlVZ8hQ/qxYsUyncB49Oj/9le5clVq1qxNjx4d2bDhV776akKW6yfoEoFxBjQaDclnTxMXFIDpkBEYVKmms11a1AbziV8jd/dE9tb/vIIgCMLH4cWL57i4uKS7PSoqiuDgDQQHb9DbplCkPEp/+fIlcrlcL2i1srLOcb38/NoxY8Z3REVF8fLlC27dusGwYSMzLGNgYEDp0u6Z7js/DKN4W+nSZXB2LsGtWzf1tuVG7/a4cZOIi4vTfl65MmUKv379BmrTDAwyfqlv4cK5NGzYGDs7B2JiYgBQq9UkJyuJiYnB1NRUr20PHNiLTCajUaOmOaq3VCqlQYNG+PsvIDExAUPDtIdv2NjYUKlSFW7dupGj4wgpRGCcBo1GQ/LpU8StWoHy+lUA4oICsZyv/yKEYRbHCwmCIBQUCWW7keTkna0yKiv99y3URlZEts941gK9Mrk4vhjg3r0QXrx4TuvWfunmsbCwpE6denTo0Flvm8m/K5ra2NigVCqJjo7WCY4jIyNyXDcfn0bMm/czBw7s4fHjx9jbO1Ktmv7cv2/6mIZSpE2TeZYccnEpqfPZ0tISAM9srCXw4MF9zp59wv79e3XSd+7cxs6d21i3bjMl3npy/Mcf+6lRo9Y73SS9OQQkN/IJ6ROB8Rs0Gg3Jp04QtyoQ5U3dO67ki+dJ/vsyBpWq5E3lBEEQ8olcewFOpkDp8Mm77yeHEhMTmTfvZxQKBW3atE83X40anxAaGkKZMh7pjj/19CyHRCLh2LEj2jHGSqWS48eP5rh+CoWC5s1bsWPHdiIjw+nc+dNMp/z8mIZSvO3OnVs8fPiAVq0yD+xTyeUGJCUlvcda6Zo6dQZJSYlvpU2mfPmKdO7cjeLF7XS2Xbt2lbCwR/TtO5CcUqvV/PnnIVxd3dLtLQZ4+fIFV678j+bNW+X4WIIIjIGUgDjp5HHiVwWiTOMRDjIZhi19kRYr/uErJwiCILwztVrN1aspM0/Ex8dx717KAh+PH4cxadK3ODg4pDu3bP/+gxkw4DPGjPmcNm3aY21tTUREOJcuXaRy5So0bdoCV1c3vL0bsGDBHJKSkrC3t2fr1k1pLozRtWs77OzsmT9/Sab19vNrT3DwBqRSqd6qb2kxMDDIVg9oehISEvjrrxMAPH36hNevX3PkyB8AVKlSHSsrKyBlJo59+3Zz9Oh/q7yGht7j/v172s/37t3lyJE/MDIy1o6XXr9+LY8fh1G1ajWsrKy5d+8ua9YEUaxYcZ0FPTJTsmRJLlw4z7lzpzE3t8De3gFLyyLvePbpS+slP4VCga2tbZq9+QcP7sPQ0FBn7PmbgoICWLUqkI0bt2NnZ8/Tp0+YPn0qTZo0x9HRiZiYaLZv38LNmzf44YefdPb7118nqV27LjY2tjx+/Ii1a1chlcr0FvwQsqdQB8YajYaEo38SsyIA1Z1b+hnkcoxa+WHcszcye4cPX0FBEAQhVyQmJjJkSF8kEgnGxibY2dlRvXpNZsyYrffo+21OTs4EBKwmIGAJc+bMIj4+nqJFbahcuSqlSv03P/3Eid8wd+5PLFmyAIVCQYsWvlSqVJVlyxbp7E+lUmV5fmNXVzecnV1wdHSi2AfsnImMjGDKFN0XuFI/L1iwFCurlCBQrVbrncvhwwe1S3BDystt+/bt1nmxzcWlBEePHubQoQPExb2mSBEr6tSpx8CBwzA3N89yPQcNGs4vv8xi8uTxxMW9ZtKkb7N0A/EhqFQqjhw5SL169bVDbt6W2n6pQyBMTEwwNTVl1apAoqIikcsN8PQsy+zZC6hVq462nL29I8+fP2PBgl+IiYnB3NycatVqMmDA4CzNfCGkT6IpxANSkp8+5W6DNO7i5HKMWvth3LMPMjv7D1+xfEwul2JlZUpk5OtMV+4RUog2yxnRbtmXm22WnJxEePgTiha1z/SFpIJALpfmy+ssLOwR3bq15/vvZ9GgQeO8ro6O/Npm+Z1oN30Zfd9YW5t+0CXaC3ePcXKyboKBAUa+bTHu0RtZcTFsQhAEQcgbr15F8fDhA1auDMDOzh4vL5+8rpIgFAqFOjCWpj7aUCj+DYg/QybGEQuCIAh57OTJ48ycOQ0nJ2emTJmGXF6of64F4YMp1P+nySwsMOnSDcNuPZHZFsvr6giCIAgCAK1a+eWbsbKCUJgU6sBYYmCAxRdfibE+giAIgiAIAvlv6RtBEARBEARByAMiMBYEQRDSVYgnLhIE4QPJT98zIjAWBEEQ9KSu8Pb2Kl+CIAi5LfV7RibL+xG+eV8DQRAEId+RSmUYG5sRGxsJgEJhmOlyxB8ztVqCSpV/eq0+BqLNcka02380Gg1JSYnExkZibGyGVJr3/bUiMBYEQRDSZGFhDaANjgsyqVSa5vLNQvpEm+WMaDd9xsZm2u+bvCYCY0EQBCFNEokES8uimJtboVIp87o6741MJsHS0oRXr+JET14WiTbLGdFu+mQyeb7oKU4lAmNBEAQhQ1KpFKm04C4LLZdLMTIyIj5eJabvzCLRZjkj2i3/yz8huiAIgiAIgiDkIREYC4IgCIIgCAIiMBYEQRAEQRAEACSa/DSrch5QqcQYn+ySyaSi3bJJtFnOiHbLPtFmOSPaLftEm+WMaLfskUolH3SqyEIfGAuCIAiCIAgCiKEUgiAIgiAIggCIwFgQBEEQBEEQABEYC4IgCIIgCAIgAmNBEARBEARBAERgLAiCIAiCIAiACIwFQRAEQRAEARCBsSAIgiAIgiAAIjAWBEEQBEEQBEAExoIgCIIgCIIAiMBYEARBEARBEAARGAuCIAiCIAgCIAJjQRAEQRAEQQBEYCwIgiAIgiAIAMjzugLvw8mTJ9m6dSv/+9//ePjwIT169OCbb75JM++KFStYt24dL168wN3dnXHjxlGrVq1Mj/HixQumT5/OsWPHkEqlNGrUiEmTJlGkSJFcPpu85eHhke6248ePU6xYsWyVtbGx4eTJk7lSt/xqwoQJbNu2TS89ICCA+vXrZ1g2OTmZBQsWsG3bNmJiYqhUqRKTJ0/G09PzfVU3X1CpVKxcuZKjR49y9+5dVCoV7u7ujBgxgjp16mRavjBca6Ghofzwww9cuHABY2NjWrduzVdffYWRkVGmZbdt28ayZcsICwujRIkSDB8+nJYtW36AWuetvXv3snPnTq5du8arV69wdnbm008/pVu3bkil6fcL9erVi7Nnz+ql79mzh1KlSr3PKue5rVu3MnHiRL30gQMH8tVXX2VYtrBeZ5D+NQMwZ84cWrduna1yBfFa++eff1ixYgX/+9//uHPnDm5ubuzatUsv39GjR5k7dy4hISHY2dnRp08fevToken+c+v3s0AGxseOHePGjRvUrFmTV69epZtvxYoVzJ07ly+++IJy5cqxadMmBg4cyKZNmzIMCJVKJQMGDCA5OZmffvoJpVLJzz//zLBhw1i3bh0SieR9nFae2Lhxo17a+PHjMTY2zjAoTtWrVy98fX21nw0MDHK1fvmVs7Mzs2fP1knLypfczJkz2b59OxMmTMDR0ZHAwED69OnDzp07sbW1fV/VzXMJCQksW7aMdu3a0b9/f+RyOdu2baNv374sWbKEhg0bZrqPgnytRUdH07t3bxwcHFiwYAERERHMnDmTqKgovevsbfv27WPChAkMGjSIevXq8ccff/DFF19gbm6Ol5fXBzqDvBEUFISDgwPjxo2jaNGinDlzhunTp/Pw4UPGjx+fYdlq1arp5XFycnqf1c1XAgMDMTc3134uXrx4hvkL83UG8O233xIbG6uTtnr1ag4cOJDpzX1hudbu3LnD0aNHqVy5Mmq1Go1Go5fn0qVLDBs2jLZt2zJhwgQuXrzIDz/8gEKhoHPnzhnuP9d+PzUFkEql0v7dsGFDzXfffaeXJzExUVO9enXNjz/+qE1TKpWali1bakaPHp3h/nfv3q1xd3fX3L59W5t24cIFjbu7u+bo0aO5cAb518OHDzXu7u6agICATPO6u7trAgMDP0Ct8pfx48drWrdune1yT58+1ZQtW1bz66+/atNiYmI0n3zyiebnn3/OzSrmO0qlUhMVFaWTplarNe3bt9f07Nkz0/IF/VpbtmyZpnLlyprw8HBt2o4dOzTu7u6au3fvZli2RYsWmpEjR+qk9evXT9O5c+f3Utf85M32SjVjxgxNxYoVNYmJiemW69mzp2bQoEHvs2r51pYtWzTu7u5ptl1GCvN1lp5GjRppBg4cmGGewnStvRmbpfc72b9/f02nTp100r7++mtNvXr1dMq/LTd/PwvkGOOMHpGlunjxIjExMTo9TDKZjFatWnH06NE072RSHT16FA8PD8qUKaNNq1atGo6Ojhw9evTdKp/P7dq1C4lEotNuQu44ceIEKpVK55GbmZkZjRo1KvDXlUwmw9LSUidNIpHg6enJ8+fP86hW+cexY8eoU6cO1tbW2rTmzZujUCgyvDYePnzIvXv39P5/9fX15e+//yYiIuK91Tk/eLO9UpUtW5bExESioqI+fIUKqMJ+naXl4sWLPHr0CD8/v7yuSr6RWWyWlJTE6dOn9Yad+Pn58eLFC65fv55u2dz8/SyQgXFWhISEAODm5qaTXqpUKV6/fs2zZ88yLJvWY/HSpUtr91tQ7d69m5o1a2JnZ5el/MuXL6d8+fLUqFGD0aNH8/jx4/dcw/zhwYMH1KhRgwoVKtChQwf++OOPTMuEhIRgY2OjN069VKlShIaGolar31Nt8ye1Ws2lS5eyPM6uIF9raX3nKBQKXFxcMvzOuXfvHpD295xGo9FuL0wuXLhAkSJFKFq0aIb5zp49S5UqVahYsSI9e/bk3LlzH6iG+YOvry9ly5alcePGLFu2DJVKlW5ecZ3p27VrF8bGxjRu3DjTvIX9Wkv14MEDkpOT9a6j0qVLA2T4XZebv58FcoxxVkRHR6NQKPReXEnttYqKiko3+IuOjtYZe5XKwsKiQAfGN2/e5Pbt20ybNi1L+du1a0eDBg2wsbHh9u3bLFmyhO7du/P777/r9Q4WJGXLlqVixYqULl2amJgYNmzYwPDhw5k/fz4tWrRIt1x615WlpSXJycnExcVhZmb2Pquer6xdu5bQ0NAsXW8F/VqLjo7GwsJCL93CwiLD9yhSt71dNrVNMipbEF25coWtW7cyfPhwZDJZuvlq1qxJ27ZtKVmyJM+fP2fFihX07duXtWvXUrVq1Q9Y4w/P1taWzz//nMqVKyORSDh8+DDz5s3j2bNn6b7ELq4zXUqlkn379tG4cWNMTEwyzFuYr7W3pXcdpX7O6DrKzd/PjyIwjomJydLjVGdnZxQKRZb3m9ZLcqlDKDJ7gS69svn9xbt3acudO3diYGBA8+bNs3SsH3/8Uft3zZo1qV69Oh06dCA4OJiBAwdmr+J5KLtt1rt3b530Ro0a0a1bNxYsWJBhYAwZX5Mfm3e51s6ePcvPP/9Mv379qFmzZqb7KCjXWnZl9Tvn7TxZ/Z4rSF68eMHIkSOpWLFiptfEyJEjdT43aNAAX19f/P39CQgIeJ/VzHPe3t54e3trP3t5eWFoaMjq1asZMmRIhi9di+ssxcmTJwkPD8/SkMPCfK2lJ73rJadxWXZ9FIHxwYMH05w+5m3bt2+nbNmyWdqnhYUFiYmJJCYmYmhoqE2Pjo4GyLCXycLCQpvvTTExMWn26uQnOW1LjUbDnj178Pb2zvGUdJ6enri6unLt2rUclc8r73r9SaVSmjVrxs8//0xCQkK602uld11FR0djYGCQac9DfpPTdrt58ybDhg2jSZMmjB07NkfH/livtfRk9J2T0VCTN3vsbGxstOmp+8rv31e5JSYmhoEDB2JkZMSSJUuyPWOJiYkJPj4+7N+//z3VMH9r2bIlK1eu5MaNG2kGxuI607Vr1y6KFCmSo9k4CvO1lt4ThqxcR7n5+/lRBMYdOnSgQ4cOubrP1B+TkJAQypUrp00PCQnB1NQ0w6lpSpUqxY0bN/TS7969m6VppfJSTtvywoULPH78OMeBSqqPsfczN66/rJx3qVKlCA8PJyoqSufmIyQkBFdX1yy9VJqf5KTdHjx4wIABAyhXrhw//fTTO/U0fYzXWnpKlSqlN0wrKSmJBw8e0LFjx3TLpY7Vu3fvnk4AHRISgkQi0RvLVxAlJiYydOhQXr58ycaNG7GyssrRfgrS9ZTbxHX2n4SEBA4dOoSfn1+Op4wsrNeai4sLBgYG3Lt3T2fO/7t37wIZT3mam7+fH9cvbS6qVq0a5ubm7NmzR5umUqnYu3cvPj4+Gf4g+/j4cPv2bZ0fqsuXLxMWFoaPj897rXde2blzJyYmJu8U+N+4cYP79+9TsWLFXKxZ/qdWq9m/fz9lypTJcDEGLy8vpFIpe/fu1aa9fv2aw4cPF9jr6k0vXrygX79+2NjY4O/vn61hUW8raNda/fr1OX36NJGRkdq0gwcPkpSUlOG14ezsjJubm873HKT0aFWqVCnNWRsKEqVSyahRo7h58yaBgYE4OjrmaD9xcXEcPXq0wFxP2bVnzx5kMplOJ9KbCvt19qbDhw/z+vXrHM9GUZivNYVCQe3atXV+AyHlOrK1tU33+oPc/f38KHqMsyssLIwrV64AEB8fz4MHD9i3bx+AdoynQqFg6NChzJ07F2tra+0CHw8fPmTOnDk6+2ratCnDhg1jxIgRADRr1gwPDw9GjhzJmDFjUKlU/PTTT1SvXl1nbFZBoVQq2b9/P02aNMHY2DjNPE2bNsXBwYHVq1cDKYunPHz4kE8++QRra2vu3LnD0qVLsbOzy3SS7o9ZWFgYEyZMwNfXFxcXF169esWGDRu4evUqCxcu1Mn7dpsVL16cbt26MXv2bORyOQ4ODqxcuRJAb9xyQZOQkMCAAQMIDw9nwoQJ2h6CVFWqVNH+XRivtW7duvHrr78ybNgwhg0bRnh4OLNmzcLPz0+nF2XSpEls375dZ1qjkSNH8sUXX+Di4kLdunU5dOgQJ0+eJDAwMC9O5YOaNm0aR44cYezYsSQkJHD58mXtttKlS2NmZqbXZufPn2fFihXa6+z58+cEBQXx4sUL5s+fn0dn8uH079+f2rVr4+7uDsChQ4cIDg7ms88+0y6SIK6z9O3cuRMHBweqV6+ut62wX2vx8fHaqdPCwsKIjY3Vxmap39/Dhw+nZ8+efP311/j5+XHx4kU2bdrEtGnTdHp93+fvZ4EMjM+cOaMztvH48eMcP34cgFu3bmnT+/Xrh0ajYe3atbx8+RJ3d3eWL1+us+qdRqNBpVLpPNqQy+UEBAQwffp0xo4di0Qi0S4JXRBfMjhx4gSRkZEZvkigUql0pkNxdXXlwIED7Nmzh9evX2NlZYWPjw+jR48u0OPNTE1NMTMzY/HixURERGBgYECFChUICAjQu2l6u80gZTlpExMT5s2bR0xMDJUrV2b16tUFetU7gJcvX3Lz5k0Ahg8frrf9zf9vC+O1ZmFhwerVq/nhhx/4/PPPMTIywtfXV2+JXrVarTetVsuWLUlISGDp0qWsWLGCEiVKMHfu3EKxGtmJEycA+Pnnn/W2rVmzhlq1aum1ma2tLUlJScyZM4eoqCiMjY2pWrUq3333HZUqVfpgdc8rrq6ubN68madPn6JWqylZsiSTJk2iV69e2jziOkvbq1evOH78OL17904zFijs11p4eDijRo3SSUv9nPr/Y9WqVfH392fOnDls374dOzs7vv76a71Ojvf5+ynRFNbBLIIgCIIgCILwhkI7xlgQBEEQBEEQ3iQCY0EQBEEQBEFABMaCIAiCIAiCAIjAWBAEQRAEQRAAERgLgiAIgiAIAiACY0EQBEEQBEEARGAsCIIgCIIgCIAIjAVBEARBEAQBKKAr3wmCkLfeXD0yI2vWrGHbtm2cPXuWw4cPv+da5Z6tW7cyceJEDh06hJOTU15XR8/ChQtZtGiR9rNcLsfW1hYvLy9GjRqV5kpQcXFx/Prrr+zZs4d//vkHjUZDiRIlaNmyJZ999hkmJiZp5t+1axePHj1Co9FQtGhRypcvT48ePfjkk0908j98+JCgoCBOnjzJkydPkEgkODk50aRJE7p3707x4sUzPa9FixaxZ88edu3apbM87LtIXdFt7dq12S47b948jh49ypYtW3KtPoIg5C2x8p0gCLnu8uXLOp/9/f05c+aMdl37VKVLlyYiIoLY2FjKlSv3AWv4biIiInjw4AHlypVDoVDkdXX0pAbGgYGBmJubExcXx4kTJwgKCsLNzY3t27djYGCgzf/y5Uv69u3LgwcP6NWrF3Xq1AHg9OnTrFmzBhcXF4KCgrCxsQFSlmPt0aMHt2/fpn///trla//55x+OHDlC9erVGTZsmHb/R44cYcyYMVhZWdGjRw/tv/Xt27e1QeX27dszPKdnz57RokULZs6cSYsWLXKtre7evQukXIvZFRMTQ6NGjZgwYQIdO3bMtToJgpCHNIIgCO/Z+PHjNVWqVMnrahQaCxYs0Li7u2vCw8N10idMmKBxd3fX/PXXXzrp/fr105QrV05z7tw5vX2dO3dOU65cOU2/fv20aX/99ZfG3d1ds3nz5jSPr1KptH8/ePBAU6VKFU27du000dHRennVarVm//79mZ7TTz/9pPH29tbZd34wbdo0TbNmzTRqtTqvqyIIQi4Qz34EQchTEyZMoFGjRjppHh4eTJs2jS1bttC8eXMqVapEhw4duHz5MhqNhsDAQBo1akTVqlX57LPP+Oeff/T2e+rUKXr37k21atWoXLky3bp146+//sq0Pmq1Gn9/f+1xa9SogZ+fn05v99atW/Hw8ODRo0fatF69euHr68vff/9N9+7dqVy5Mo0bN2b58uWo1WqdY0RHRzNr1iwaN25MhQoVqFOnDgMHDiQkJESbJykpCX9/f1q0aEGFChWoXbs2EydOJCIiIstt+7YKFSoAEB4erk27cuUKJ06coGPHjtSoUUOvTI0aNejYsSMnTpzg6tWrAERFRQGkOSQD0BlWsGrVKuLi4vj2228xNzfXyyuRSGjWrFmG9U5KSmLz5s34+vrq7PvRo0d4eHgQGBjI8uXLadSoEZUqVaJXr16EhoaSnJzM7Nmz8fLyonr16gwfPlzn3CHl3y11OMWb+1yxYgVBQUHa66xr1656T0IA2rZty/379zl9+nSG5yAIwsdBjDEWBCFf+vPPP7l+/TpfffUVEomEn3/+mcGDB9OuXTsePnzIN998Q0xMDLNmzeLzzz/n999/RyKRAPD7778zfvx4GjduzI8//ohcLmfjxo3079+fFStWaIcKpCUwMJBFixYxdOhQatSogVKp5N69e8TExGRa5xcvXjB27Fj69u3LiBEjOHjwIL/88gvFihWjXbt2AMTGxtK9e3fCwsIYMGAAlStXJi4ujnPnzvHixQtKlSqFWq1m2LBhXLhwgf79+1OtWjXCwsJYuHAhf//9N1u2bMHIyCjbbZoayJcsWVKbdurUKQCaNGmSbrnGjRuzceNGTp06RYUKFahQoQIGBgZMnz6dqKgoateuTbFixdIse+LECWxsbKhSpUq265vq77//Jioqilq1aqW5ff369bi7u/PNN98QHR3Njz/+yJAhQ6hcuTJyuZwZM2bw+PFjfvzxRyZPnszSpUszPea6detwc3Nj0qRJAMyfP59BgwZx6NAhnQC/fPnymJiYcPTo0QyvK0EQPg4iMBYEIV9KSkpi5cqVOi99DR8+nDNnzrBt2zZtEBwREcGMGTO4ffs2Hh4exMfHM2PGDBo0aMDixYu1ZX18fGjfvj1z5sxh06ZN6R734sWLuLu78/nnn2vTvL29s1TnqKgoAgICtGNu69aty9mzZ9m5c6c2MF69ejV37twhKCiIunXrasu+2Wu6d+9ejh8/zsKFC3XSPT096dSpE1u3bqV79+6Z1ketVqNUKomLi+PkyZP89ttv+Pr6Ur58eW2ex48fA2T4EmHqtjfzTp06lenTpzN27Fggpfe4Xr16dO7cWafn+cmTJ5QtWzbTumbk0qVLADr1fpO5uTn+/v7a3uTIyEhmzJiBm5sbS5Ys0ea7d+8eq1evJjY2FjMzswyPaWpqyrJly5DJZAAUK1aMzp07c+zYMVq3bq3NJ5PJ8PT05OLFi+90joIg5A9iKIUgCPlSrVq1dILiUqVKAVC/fn1tUPxmemrQdunSJaKiomjfvj1KpVL7n1qtxtvbmytXrhAXF5fucStWrMjNmzeZOnUqx48fJzY2Nst1trW11QbFqTw8PLR1Azh+/DglS5bUCYrfduTIESwsLGjYsKHOOZQtWxZbW1vOnj2bpfrUq1eP8uXLU7NmTUaPHk358uWZNWtWls/nbW+2e6dOnTh27Bi//PILvXr1wt7enh07dtCzZ08CAwNzfIy0PH/+HIlEgpWVVZrbfXx8dIZYpF4TDRo00Mn39rWSkQYNGmiDYki5KQEICwvTy1u0aFGePXuW6T4FQcj/RI+xIAj5kqWlpc7n1FkU0ktPTEwEUmZYABg5cmS6+3716pXe9GOpBg8ejImJCTt27OC3335DJpNRo0YNvvrqKypWrJhhnYsUKaKXplAotHWDlB5ue3v7DPcTHh5OdHS0dkzw2yIjIzMsn2rVqlWYmZnx6tUrgoOD2b9/P99//z3Tpk3T5nFwcABShlm4ubmluZ/UIRhv19vc3BxfX198fX0BuHPnDn379mXevHl06dIFCwsL7O3tdcZi50RiYiJyuVwnUH1TTq+VjLz9b5k6+0haZQ0NDUlISMh0n4Ig5H8iMBYEoUBJ7VWcMmUKlStXTjNP0aJF0y0vl8vp27cvffv2JTo6mlOnTjF37lwGDBjAn3/+ibGx8TvVz9ramqdPn2aYx8rKiiJFiqTb82pqapqlY3l4eGBtbQ2k9B7379+f4OBgOnXqpDPcY86cOfzxxx/Ur18/zf0cOnRImzcjZcqUoVWrVqxevZr79+9TqVIlvL29Wbt2LZcvX87xOGMrKyuSk5OJi4tL94YmL0VFRaXbmy0IwsdFDKUQBKFAqVatGhYWFty9e5eKFSum+V9W5x62sLCgRYsWdO/enaioqDQfo2eXt7c39+/fz3CGjAYNGhAVFYVarU6z/un17GZEIpHwzTffIJPJmD9/vja9YsWKeHl5sWXLFi5cuKBX7vz582zZsgVvb29tD3ZkZCRJSUlpHufevXsA2pfxevfujYmJCd99912aLzBqNBoOHjyYYd1dXV0BePDgQRbO9MN79OhRjuZBFgQh/xE9xoIgFCimpqZ8/fXXTJgwgVevXtG8eXOKFi1KREQEN2/eJCIigu+++y7d8kOGDKFMmTJUqFABa2trwsLCWL16NY6OjpQoUeKd69e7d2/27t3LsGHDGDRoEJUqVSIhIYFz587RoEEDateuTevWrdm5cyeDBg2iV69eVKpUCQMDA54+fcqZM2do3LgxTZs2zfaxS5YsSZcuXVi/fj3nz5/XviT3448/0rdvX/r375/mAh9ubm46Y5PPnDnD9OnT8fPzo1q1ahQpUoTw8HB2797N8ePHadeuHXZ2dgA4OzszZ84cvvjiC9q2bUvPnj21L+OFhISwZcsWNBpNhueTOhvF//73P+1Y3/wiMjKS+/fv07Nnz7yuiiAIuUAExoIgFDht27bFwcGBwMBAvv32W16/fo21tTVly5alffv2GZatVasW+/fvZ9OmTcTGxmJra0vdunUZNmyYzmpxOWVmZsb69etZuHAhwcHBLF68GAsLCypWrEiXLl2AlJkOlixZwpo1a/j9999Zvnw5MpkMOzs7atasibu7e46PP2LECLZv386CBQtYs2YNADY2NmzcuJG1a9eyd+9e7fLILi4uDB48WNvrm6pKlSp07NiRM2fOsGPHDiIjIzE0NKR06dJMmTKFTz/9VOeYDRs2ZOfOnaxcuZLffvuNJ0+eIJVKcXJywtvbO9Og0t7enho1anDo0CG6du2a43N/Hw4dOoSBgQEtW7bM66oIgpALxJLQgiAIQr63f/9+vvjiC44cOULx4sXzujpa3bt3x97enl9++SWvqyIIQi4QY4wFQRCEfK9Zs2ZUrFiRZcuW5XVVtM6dO8eVK1cYPXp0XldFEIRcIgJjQRAEId+TSCR8//33FCtWTG+J7bwSFRXFjz/+iLOzc15XRRCEXCKGUgiCIAiCIAgCosdYEARBEARBEAARGAuCIAiCIAgCIAJjQRAEQRAEQQBEYCwIgiAIgiAIgAiMBUEQBEEQBAEQgbEgCIIgCIIgACIwFgRBEARBEARABMaCIAiCIAiCAMD/Ae3AGIerWyGHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate pre-ROSC model slopes and intercepts\n",
    "#Get df for cases at t=0 and after (Post-ROSC)\n",
    "df2_pre = df2_sns[df2_sns['time'] < 0]\n",
    "\n",
    "# Survived------------------------------------\n",
    "df2_survived = df2_pre[df2_pre['ROSC-Survived-CPC-1or2'] == 'yes'].copy()\n",
    "md_survived = smf.mixedlm(\"b_ox ~ time\", df2_survived, groups=df2_survived[\"patient\"])\n",
    "md_survived_f = md_survived.fit()\n",
    "# print(md_survived_f.summary())\n",
    "\n",
    "surv_intercept_pre = round(md_survived_f.params['Intercept'], 2)\n",
    "surv_time_coef_pre = round(md_survived_f.params['time'], 2)\n",
    "\n",
    "#Died----------------------------------------\n",
    "df2_died = df2_pre[df2_pre['ROSC-Survived-CPC-1or2'] == 'no'].copy()\n",
    "md_died = smf.mixedlm(\"b_ox ~ time\", df2_died, groups=df2_died[\"patient\"])\n",
    "md_died_f = md_died.fit()\n",
    "# print(md_died_f.summary())\n",
    "\n",
    "died_intercept_pre = round(md_died_f.params['Intercept'], 2)\n",
    "died_time_coef_pre = round(md_died_f.params['time'], 2)\n",
    "\n",
    "# Plot it -------------------------------------\n",
    "# Define the slope and intercept for the two functions\n",
    "m1pre, B1pre = surv_time_coef_pre, surv_intercept_pre  # Function 1: y = m1 * t + B1\n",
    "m2pre, B2pre = died_time_coef_pre, died_intercept_pre  # Function 2: y = m2 * t + B2\n",
    "# Generate values for t\n",
    "tpre = np.linspace(-10, 0, 100)\n",
    "# Calculate the corresponding y values for both functions\n",
    "y1pre = m1pre * tpre + B1pre\n",
    "y2pre = m2pre * tpre + B2pre\n",
    "\n",
    "\n",
    "# Set up the plot\n",
    "sns.set_theme(rc={'figure.figsize':(8,4)})\n",
    "sns.lineplot(x=t, y=y1, label=f'Survived: y = {m1}*t + {B1}', color='tab:olive')\n",
    "sns.lineplot(x=tpre, y=y1pre, label=f'Survived: y = {m1pre}*t + {B1pre}', color='tab:green', linewidth=2.5)\n",
    "sns.lineplot(x=trosc, y=y1rosc, label=f'Survived: y = {m1rosc}*t + {B1rosc}', color='lightseagreen', linewidth=2.5)\n",
    "sns.lineplot(x=t, y=y2, label=f'Died: y = {m2}*t + {B2}', color='tab:purple')\n",
    "sns.lineplot(x=tpre, y=y2pre, label=f'Died: y = {m2pre}*t + {B2pre}', color='tab:red', linestyle='--', linewidth=2.5)\n",
    "sns.lineplot(x=trosc, y=y2rosc, label=f'Died: y = {m2rosc}*t + {B2rosc}', color='tab:orange', linestyle='--', linewidth=2.5)\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Mixed Effects Regression Models pre- and post-ROSC')\n",
    "plt.xlabel('Time since ROSC (min)')\n",
    "plt.ylabel('rSO2 (%)')\n",
    "plt.xlim(-10,10)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4ae325-5289-436e-beb5-fe693151d452",
   "metadata": {},
   "source": [
    "## Quadratic Mixed Effects Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "58e5e47c-a849-4f9f-9ff1-a8d6e882fc0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mixed Linear Model Regression Results\n",
      "=======================================================\n",
      "Model:            MixedLM Dependent Variable: b_ox     \n",
      "No. Observations: 91      Method:             REML     \n",
      "No. Groups:       8       Scale:              79.7569  \n",
      "Min. group size:  9       Log-Likelihood:     -339.9817\n",
      "Max. group size:  14      Converged:          Yes      \n",
      "Mean group size:  11.4                                 \n",
      "-------------------------------------------------------\n",
      "             Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "-------------------------------------------------------\n",
      "Intercept    56.147    4.956 11.328 0.000 46.433 65.861\n",
      "time          2.303    0.423  5.443 0.000  1.474  3.133\n",
      "time_2       -0.207    0.055 -3.777 0.000 -0.314 -0.099\n",
      "Group Var   181.679   11.874                           \n",
      "=======================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_survived_quad = df2_sns[df2_sns['ROSC-Survived-CPC-1or2'] == 'yes'].copy()\n",
    "df2_survived_quad['time_2'] = df2_survived_quad['time'] ** 2\n",
    "md_survived_quad = smf.mixedlm(\"b_ox ~ time + time_2\", df2_survived_quad, groups=df2_survived_quad[\"patient\"])\n",
    "md_survived_f_quad = md_survived_quad.fit()\n",
    "print(md_survived_f_quad.summary())\n",
    "\n",
    "surv_intercept_quad = round(md_survived_f_quad.params['Intercept'], 2)\n",
    "surv_time_coef_t = round(md_survived_f_quad.params['time'], 2)\n",
    "surv_time_coef_t2 = round(md_survived_f_quad.params['time_2'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c388abc4-5b4e-4cc2-aa03-3650deecefb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mixed Linear Model Regression Results\n",
      "========================================================\n",
      "Model:            MixedLM Dependent Variable: b_ox      \n",
      "No. Observations: 305     Method:             REML      \n",
      "No. Groups:       22      Scale:              70.4111   \n",
      "Min. group size:  5       Log-Likelihood:     -1122.0307\n",
      "Max. group size:  21      Converged:          Yes       \n",
      "Mean group size:  13.9                                  \n",
      "--------------------------------------------------------\n",
      "              Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "--------------------------------------------------------\n",
      "Intercept     47.063    3.028 15.541 0.000 41.128 52.998\n",
      "time           1.416    0.104 13.622 0.000  1.212  1.620\n",
      "time_2        -0.029    0.017 -1.695 0.090 -0.062  0.004\n",
      "Group Var    191.712    7.526                           \n",
      "========================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_died_quad = df2_sns[df2_sns['ROSC-Survived-CPC-1or2'] == 'no'].copy()\n",
    "df2_died_quad['time_2'] = df2_died_quad['time'] ** 2\n",
    "md_died_quad = smf.mixedlm(\"b_ox ~ time + time_2\", df2_died_quad, groups=df2_died_quad[\"patient\"])\n",
    "md_died_f_quad = md_died_quad.fit()\n",
    "print(md_died_f_quad.summary())\n",
    "\n",
    "died_intercept_quad = round(md_died_f_quad.params['Intercept'], 2)\n",
    "died_time_coef_t = round(md_died_f_quad.params['time'], 2)\n",
    "died_time_coef_t2 = round(md_died_f_quad.params['time_2'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a094d6c0-7f76-4331-a5db-cc57792700c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the slope and intercept for the two functions\n",
    "s1, s2, sB = surv_time_coef_t, surv_time_coef_t2, surv_intercept_quad  # Function 1: y = m1 * t + m2 * t^2 + B1\n",
    "d1, d2, dB = died_time_coef_t, died_time_coef_t2, died_intercept_quad  # Function 2: y = m2 * t + m2 * t^2 + B2\n",
    "\n",
    "# Generate values for t\n",
    "t = np.linspace(-10, 10, 100)\n",
    "t2 = t ** 2\n",
    "\n",
    "# Calculate the corresponding y values for both functions\n",
    "sy = s1 * t + s2 * t2 + sB\n",
    "dy = d1 * t + d2 * t2 + dB\n",
    "\n",
    "# Set up the plot\n",
    "sns.set_theme(rc={'figure.figsize':(10,4)})\n",
    "sns.lineplot(x=t, y=sy, label=f'Survived-quad: y = {s1}*t + + {s2}*t^2 + {sB}', color='blue')\n",
    "sns.lineplot(x=t, y=dy, label=f'Died-quad: y = {d1}*t + {d2}*t^2 + {dB}', color='orange')\n",
    "sns.lineplot(x=t, y=y1, label=f'Survived-linear: y = {m1}*t + {B1}', color='tab:olive')\n",
    "sns.lineplot(x=t, y=y2, label=f'Died-linear: y = {m2}*t + {B2}', color='tab:purple')\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Mixed Effects Quadratic Regression from [-10, 10] min')\n",
    "plt.xlabel('Time since ROSC (min)')\n",
    "plt.ylabel('rSO2 (%)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94110e68-d223-4f17-900e-74182c6faff1",
   "metadata": {},
   "source": [
    "## Cubic Mixed Effects Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ec24cd1f-9fcf-4c11-9fa8-bb29bd8c7654",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived\n",
    "df2_survived_cube = df2_sns[df2_sns['ROSC-Survived-CPC-1or2'] == 'yes'].copy()\n",
    "df2_survived_cube['time_2'] = df2_survived_cube['time'] ** 2\n",
    "df2_survived_cube['time_3'] = df2_survived_cube['time'] ** 3\n",
    "md_survived_cube = smf.mixedlm(\"b_ox ~ time + time_2 + time_3\", df2_survived_cube, groups=df2_survived_cube[\"patient\"])\n",
    "md_survived_f_cube = md_survived_cube.fit()\n",
    "# print(md_survived_f_cube.summary())\n",
    "\n",
    "surv_intercept_cube = round(md_survived_f_cube.params['Intercept'], 2)\n",
    "surv_time_coef_cubet = round(md_survived_f_cube.params['time'], 2)\n",
    "surv_time_coef_cubet2 = round(md_survived_f_cube.params['time_2'], 2)\n",
    "surv_time_coef_cubet3 = round(md_survived_f_cube.params['time_3'], 2)\n",
    "\n",
    "# Died\n",
    "df2_died_cube = df2_sns[df2_sns['ROSC-Survived-CPC-1or2'] == 'no'].copy()\n",
    "df2_died_cube['time_2'] = df2_died_cube['time'] ** 2\n",
    "df2_died_cube['time_3'] = df2_died_cube['time'] ** 3\n",
    "md_died_cube = smf.mixedlm(\"b_ox ~ time + time_2 + time_3\", df2_died_cube, groups=df2_died_cube[\"patient\"])\n",
    "md_died_f_cube = md_died_cube.fit()\n",
    "# print(md_died_f_cube.summary())\n",
    "\n",
    "died_intercept_cube = round(md_died_f_cube.params['Intercept'], 2)\n",
    "died_time_coef_cubet = round(md_died_f_cube.params['time'], 2)\n",
    "died_time_coef_cubet2 = round(md_died_f_cube.params['time_2'], 2)\n",
    "died_time_coef_cubet3 = round(md_died_f_cube.params['time_3'], 2)\n",
    "\n",
    "\n",
    "# Define the slope and intercept for the two functions\n",
    "sc1, sc2, sc3, scB = surv_time_coef_cubet, surv_time_coef_cubet2, surv_time_coef_cubet3, surv_intercept_cube  # Function 1: y = m1 * t + m2 * t^2 + B1\n",
    "dc1, dc2, dc3, dcB = died_time_coef_cubet, died_time_coef_cubet2, died_time_coef_cubet3, died_intercept_cube  # Function 2: y = m2 * t + m2 * t^2 + B2\n",
    "\n",
    "# Generate values for t\n",
    "t = np.linspace(-10, 10, 100)\n",
    "t2 = t ** 2\n",
    "t3 = t ** 3\n",
    "\n",
    "# Calculate the corresponding y values for both functions\n",
    "scy = sc1 * t + sc2 * t2 + sc3 * t3 + scB\n",
    "dcy = dc1 * t + dc2 * t2 + dc3 * t3 + dcB\n",
    "\n",
    "# Set up the plot\n",
    "sns.set_theme(rc={'figure.figsize':(10,4)})\n",
    "sns.lineplot(x=t, y=scy, label=f'Survived-cubic: y = {sc1}*t + + {sc2}*t^2 + {sc3}*t^3 + {scB}', color='green')\n",
    "sns.lineplot(x=t, y=dcy, label=f'Died-cubic: y = {dc1}*t + {dc2}*t^2 + {dc3}*t^3 + {dcB}', color='red')\n",
    "sns.lineplot(x=t, y=sy, label=f'Survived-quad: y = {s1}*t + + {s2}*t^2 + {sB}', color='blue')\n",
    "sns.lineplot(x=t, y=dy, label=f'Died-quad: y = {d1}*t + {d2}*t^2 + {dB}', color='orange')\n",
    "sns.lineplot(x=t, y=y1, label=f'Survived-linear: y = {m1}*t + {B1}', color='tab:olive')\n",
    "sns.lineplot(x=t, y=y2, label=f'Died-linear: y = {m2}*t + {B2}', color='tab:purple')\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Mixed Effects Quadratic Regression from [-10, 10] min')\n",
    "plt.xlabel('Time since ROSC (min)')\n",
    "plt.ylabel('rSO2 (%)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abeb3390-0b67-4090-8984-5181e7ae385f",
   "metadata": {},
   "source": [
    "## Linear Regression + Logistic Regression\n",
    "For each case,\n",
    "1. Get BOx and time as vectors\n",
    "2. Fit a linear model\n",
    "3. Get m (slope) and B (intercept), save to lists\n",
    "  \n",
    "Fit m and B to logistic model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486f86b6-0a68-4307-b5b3-46ceb20604df",
   "metadata": {},
   "source": [
    "### Make logistic regression data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5ee163fd-4b9e-4be0-92b9-c627ef012c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slopes</th>\n",
       "      <th>intercepts</th>\n",
       "      <th>ROSC-CPC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.087013</td>\n",
       "      <td>49.047619</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.257143</td>\n",
       "      <td>66.038095</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.093407</td>\n",
       "      <td>43.835165</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.593506</td>\n",
       "      <td>29.285714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.851948</td>\n",
       "      <td>23.904762</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     slopes  intercepts  ROSC-CPC12\n",
       "0 -0.087013   49.047619           0\n",
       "1  0.257143   66.038095           0\n",
       "2  2.093407   43.835165           0\n",
       "3  1.593506   29.285714           0\n",
       "4  0.851948   23.904762           0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slopes = []\n",
    "intercepts = []\n",
    "survival = []\n",
    "\n",
    "cases = df2_sns['patient'].unique()\n",
    "for case in cases:\n",
    "    df_case = df2_sns[df2_sns['patient'] == case]\n",
    "\n",
    "    # Define the independent variable (X) and dependent variable (Y)\n",
    "    X = df_case['time']\n",
    "    Y = df_case['b_ox']\n",
    "    # Add a constant to the independent variable\n",
    "    X = sm.add_constant(X)\n",
    "    # Fit the model\n",
    "    model = sm.OLS(Y, X).fit()\n",
    "    # Review the results\n",
    "    # print(model.summary())\n",
    "    # Save coefficients from the model\n",
    "    intercept = model.params['const']\n",
    "    slope = model.params['time']\n",
    "\n",
    "    slopes.append(slope)\n",
    "    intercepts.append(intercept)\n",
    "    survs = df_case['ROSC-Survived-CPC-1or2'].to_list()\n",
    "    surv = survs[0]\n",
    "    if surv == 'no':\n",
    "        survival.append(0)\n",
    "    else:\n",
    "        survival.append(1)\n",
    "\n",
    "dflogi = pd.DataFrame(slopes, columns=['slopes'])\n",
    "dflogi['intercepts'] = intercepts\n",
    "dflogi['ROSC-CPC12'] = survival\n",
    "\n",
    "dflogi[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6845cd08-3f76-4395-ae24-ae102d66131e",
   "metadata": {},
   "source": [
    "### Fit the logistic model - Get Adjusted ORs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2e84e3c2-2f09-4c36-9167-43abbfec0364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.457813\n",
      "         Iterations 7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OLS Component</th>\n",
       "      <th>AORs [95% CI]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>0.03 [0.0, 4.38]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>slopes</td>\n",
       "      <td>0.57 [0.239, 1.34]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>intercepts</td>\n",
       "      <td>1.07 [0.974, 1.17]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  OLS Component       AORs [95% CI]\n",
       "0         const    0.03 [0.0, 4.38]\n",
       "1        slopes  0.57 [0.239, 1.34]\n",
       "2    intercepts  1.07 [0.974, 1.17]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the features (X) and target variable (y)\n",
    "X = dflogi[['slopes', 'intercepts']]\n",
    "y = dflogi['ROSC-CPC12']\n",
    "# Add a constant to the features (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Fit the logistic regression model\n",
    "model = sm.Logit(y, X)\n",
    "result = model.fit()\n",
    "# Print the summary of the model\n",
    "# print(result.summary())\n",
    "\n",
    "coeffs = result.params.tolist()\n",
    "pvalues = result.pvalues.tolist()\n",
    "conf_intervals = result.conf_int(alpha=0.025)  # alpha = 1 - confidence level\n",
    "conf_intervals.columns = ['0.025', '0.975']\n",
    "lower = conf_intervals['0.025'].tolist()\n",
    "upper = conf_intervals['0.975'].tolist()\n",
    "\n",
    "# Make a results dataframe with Adjusted Odds Ratios------------------------\n",
    "aors = []\n",
    "ps = []\n",
    "lowers = []\n",
    "uppers = []\n",
    "tablecol = []\n",
    "for i in range(len(coeffs)):\n",
    "    aors.append(np.exp(coeffs[i]))\n",
    "    ps.append(pvalues[i])\n",
    "    lowers.append(np.exp(lower[i]))\n",
    "    uppers.append(np.exp(upper[i]))\n",
    "    tablecol.append(str(round(aors[i], 2)) + ' [' + str(round(lowers[i], 3)) + ', ' + str(round(uppers[i], 2)) + ']')\n",
    "\n",
    "results_df = pd.DataFrame(['const', 'slopes', 'intercepts'], columns=['OLS Component'])\n",
    "results_df['AORs [95% CI]'] = tablecol\n",
    "# results_df['p'] = ps\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e19f1c3-d249-4ebc-a74e-ff720227c61b",
   "metadata": {},
   "source": [
    "# 3 - Differences in Medians\n",
    "Pre- vs. Post-ROSC and CPC12 vs. Not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "55c8e6e4-f42a-450e-a92c-7d482f0cd95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate results df\n",
    "chars = ['n', 'Pre-ROSC Median', 'Post-ROSC Median', 'Pre-ROSC Median (IQR)', 'Post-ROSC Median (IQR)']\n",
    "medians_df = pd.DataFrame(chars, columns=['Characteristic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b41e6c25-6b00-4d68-ac09-e2756815743f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dataframes for all combinations\n",
    "\n",
    "# ROSC\n",
    "dfrosc = df[df['ROSC'] == 1][['CASSID', 'ROSC-CPC12', 'Witnessed',\n",
    "                              'Pre-ROSC Medians (-5,-2)', 'Post-ROSC Medians (0,5)', 'Pre-ROSC Lowers (-5,-2)', 'Pre-ROSC Uppers (-5,-2)', \n",
    "                              'Post-ROSC Lowers (0,5)', 'Post-ROSC Uppers (0,5)', 'Pre-ROSC Vals (-5,-2) (rSO2 %)', \n",
    "                              'Post-ROSC Vals (0,5) (rSO2 %)', 'Post-ROSC Medians (0,2)', 'Post-ROSC Lowers (0,2)', \n",
    "                              'Post-ROSC Uppers (0,2)', 'Difference (0,2) (rSO2 %)', 'Post-ROSC Vals (0,2) (rSO2 %)', \n",
    "                              'Post-ROSC Medians (0,3)', 'Post-ROSC Lowers (0,3)', 'Post-ROSC Uppers (0,3)', \n",
    "                              'Difference (0,3) (rSO2 %)', 'Post-ROSC Vals (0,3) (rSO2 %)', 'Difference (0,5) (rSO2 %)']].copy()\n",
    "dfrosc.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dfrosccpc12 = dfrosc[dfrosc['ROSC-CPC12'] == 1].copy()\n",
    "dfrosccpc12.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dfroscnot = dfrosc[dfrosc['ROSC-CPC12'] == 0].copy()\n",
    "dfroscnot.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Witnessed\n",
    "dfw = dfrosc[dfrosc['Witnessed'] == 1].copy()\n",
    "dfw.reset_index(inplace=True, drop=True)\n",
    "\n",
    "dfwcpc12 = dfw[dfw['ROSC-CPC12'] == 1].copy()\n",
    "dfwcpc12.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dfwnot = dfw[dfw['ROSC-CPC12'] == 0].copy()\n",
    "dfwnot.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# BOTH pre- AND post-ROSC medians\n",
    "dfroscdiff = dfrosc[np.isnan(dfrosc['Difference (0,5) (rSO2 %)']) == False].copy()\n",
    "dfroscdiff.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dfroscdiffcpc12 = dfroscdiff[dfroscdiff['ROSC-CPC12'] == 1].copy()\n",
    "dfroscdiffcpc12.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dfroscdiffnot = dfroscdiff[dfroscdiff['ROSC-CPC12'] == 0].copy()\n",
    "dfroscdiffnot.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Witnessed, BOTH pre- AND post-ROSC medians\n",
    "dfdiffw = dfroscdiff[dfroscdiff['Witnessed'] == 1].copy()\n",
    "dfdiffw.reset_index(inplace=True, drop=True)\n",
    "\n",
    "dfdiffwcpc12 = dfdiffw[dfdiffw['ROSC-CPC12'] == 1].copy()\n",
    "dfdiffwcpc12.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dfdiffwnot = dfdiffw[dfdiffw['ROSC-CPC12'] == 0].copy()\n",
    "dfdiffwnot.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a078e6fa-53b8-49c8-bed4-0cffb0aafada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     15.0\n",
       "1     -7.0\n",
       "2     10.0\n",
       "3     10.0\n",
       "4     21.0\n",
       "5     15.0\n",
       "6     -1.0\n",
       "7     20.0\n",
       "8     12.0\n",
       "9      0.0\n",
       "10    -3.0\n",
       "11    44.0\n",
       "12     6.0\n",
       "13    22.0\n",
       "14    14.0\n",
       "15     9.0\n",
       "16     3.0\n",
       "17     9.0\n",
       "Name: Difference (0,5) (rSO2 %), dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfdiffwnot['Difference (0,5) (rSO2 %)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d02c1dae-d412-4be9-8b73-53ea61162cf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "12.0\n",
      "6.0\n",
      "20.5\n",
      "13.91304347826087\n"
     ]
    }
   ],
   "source": [
    "print(len(dfdiffw['Difference (0,5) (rSO2 %)']))\n",
    "print(dfdiffw['Difference (0,5) (rSO2 %)'].median())\n",
    "print(dfdiffw['Difference (0,5) (rSO2 %)'].quantile(0.25))\n",
    "print(dfdiffw['Difference (0,5) (rSO2 %)'].quantile(0.75))\n",
    "print(dfdiffw['Difference (0,5) (rSO2 %)'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1d98511-f066-4472-b836-1c3f7477ec88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "29.0\n",
      "13.0\n",
      "34.0\n",
      "24.2\n"
     ]
    }
   ],
   "source": [
    "print(len(dfdiffwcpc12['Difference (0,5) (rSO2 %)']))\n",
    "print(dfdiffwcpc12['Difference (0,5) (rSO2 %)'].median())\n",
    "print(dfdiffwcpc12['Difference (0,5) (rSO2 %)'].quantile(0.25))\n",
    "print(dfdiffwcpc12['Difference (0,5) (rSO2 %)'].quantile(0.75))\n",
    "print(dfdiffwcpc12['Difference (0,5) (rSO2 %)'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d9f66c7d-9aa1-46da-b344-baa56800dcb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n",
      "10.0\n",
      "3.75\n",
      "15.0\n",
      "11.055555555555555\n"
     ]
    }
   ],
   "source": [
    "print(len(dfdiffwnot['Difference (0,5) (rSO2 %)']))\n",
    "print(dfdiffwnot['Difference (0,5) (rSO2 %)'].median())\n",
    "print(dfdiffwnot['Difference (0,5) (rSO2 %)'].quantile(0.25))\n",
    "print(dfdiffwnot['Difference (0,5) (rSO2 %)'].quantile(0.75))\n",
    "print(dfdiffwnot['Difference (0,5) (rSO2 %)'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "92b21750-2f01-468e-be09-a4c6e8d0bc55",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = [dfrosc, dfrosccpc12, dfroscnot, dfw, dfwcpc12, dfwnot, dfroscdiff, dfroscdiffcpc12, dfroscdiffnot, dfdiffw, dfdiffwcpc12, dfdiffwnot]\n",
    "colnames = ['All ROSC Cases', 'ROSC, CPC 1 or 2', 'ROSC, Not', 'ROSC, All Witnessed', 'ROSC, Witnessed, CPC 1 or 2',\n",
    "       'ROSC, Witnessed, Not', 'ROSC, pre&post meds', 'ROSC, pre&post meds, CPC 1 or 2', 'ROSC, pre&post meds, Not',\n",
    "       'ROSC, witnessed, pre&post meds', 'ROSC, Witnessed, pre&post meds, CPC 1 or 2', 'ROSC, Witnessed, pre&post meds, Not']\n",
    "\n",
    "for i in range(len(dfs)):\n",
    "    d = dfs[i]\n",
    "    col = []\n",
    "    col.append(len(d))\n",
    "    col.append(d['Pre-ROSC Medians (-5,-2)'].median())\n",
    "    col.append(d['Post-ROSC Medians (0,5)'].median())\n",
    "    pre = str(d['Pre-ROSC Medians (-5,-2)'].median()) + ' (' + str(d['Pre-ROSC Medians (-5,-2)'].quantile(0.25)) + ', ' + str(d['Pre-ROSC Medians (-5,-2)'].quantile(0.75)) + ')'\n",
    "    col.append(pre)\n",
    "    post = str(d['Post-ROSC Medians (0,5)'].median()) + ' (' +str(d['Post-ROSC Medians (0,5)'].quantile(0.25)) + ', ' + str(d['Post-ROSC Medians (0,5)'].quantile(0.75)) + ')'\n",
    "    col.append(post)\n",
    "    medians_df[colnames[i]] = col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ce38b48c-9c41-4f56-992b-7e6fa21936c6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Characteristic</th>\n",
       "      <th>All ROSC Cases</th>\n",
       "      <th>ROSC, CPC 1 or 2</th>\n",
       "      <th>ROSC, Not</th>\n",
       "      <th>ROSC, All Witnessed</th>\n",
       "      <th>ROSC, Witnessed, CPC 1 or 2</th>\n",
       "      <th>ROSC, Witnessed, Not</th>\n",
       "      <th>ROSC, pre&amp;post meds</th>\n",
       "      <th>ROSC, pre&amp;post meds, CPC 1 or 2</th>\n",
       "      <th>ROSC, pre&amp;post meds, Not</th>\n",
       "      <th>ROSC, witnessed, pre&amp;post meds</th>\n",
       "      <th>ROSC, Witnessed, pre&amp;post meds, CPC 1 or 2</th>\n",
       "      <th>ROSC, Witnessed, pre&amp;post meds, Not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>n</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pre-ROSC Median</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Post-ROSC Median</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre-ROSC Median (IQR)</td>\n",
       "      <td>39.5 (23.0, 52.75)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>40.0 (22.0, 53.0)</td>\n",
       "      <td>39.0 (28.0, 52.5)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>39.0 (23.75, 52.75)</td>\n",
       "      <td>39.5 (23.0, 52.75)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>40.0 (22.0, 53.0)</td>\n",
       "      <td>39.0 (28.0, 52.5)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>39.0 (23.75, 52.75)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Post-ROSC Median (IQR)</td>\n",
       "      <td>52.0 (41.75, 66.75)</td>\n",
       "      <td>63.0 (58.0, 73.0)</td>\n",
       "      <td>51.0 (38.5, 64.0)</td>\n",
       "      <td>54.0 (43.25, 63.75)</td>\n",
       "      <td>63.0 (57.5, 67.75)</td>\n",
       "      <td>51.0 (43.0, 62.75)</td>\n",
       "      <td>52.0 (43.0, 65.5)</td>\n",
       "      <td>63.0 (58.0, 66.0)</td>\n",
       "      <td>51.0 (41.0, 64.0)</td>\n",
       "      <td>52.0 (43.5, 63.5)</td>\n",
       "      <td>63.0 (58.0, 66.0)</td>\n",
       "      <td>51.0 (43.0, 62.75)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Characteristic       All ROSC Cases   ROSC, CPC 1 or 2  \\\n",
       "0                       n                   51                  9   \n",
       "1         Pre-ROSC Median                 39.5               39.0   \n",
       "2        Post-ROSC Median                 52.0               63.0   \n",
       "3   Pre-ROSC Median (IQR)   39.5 (23.0, 52.75)  39.0 (32.0, 45.0)   \n",
       "4  Post-ROSC Median (IQR)  52.0 (41.75, 66.75)  63.0 (58.0, 73.0)   \n",
       "\n",
       "           ROSC, Not  ROSC, All Witnessed ROSC, Witnessed, CPC 1 or 2  \\\n",
       "0                 42                   31                           8   \n",
       "1               40.0                 39.0                        39.0   \n",
       "2               51.0                 54.0                        63.0   \n",
       "3  40.0 (22.0, 53.0)    39.0 (28.0, 52.5)           39.0 (32.0, 45.0)   \n",
       "4  51.0 (38.5, 64.0)  54.0 (43.25, 63.75)          63.0 (57.5, 67.75)   \n",
       "\n",
       "  ROSC, Witnessed, Not ROSC, pre&post meds ROSC, pre&post meds, CPC 1 or 2  \\\n",
       "0                   23                  38                               5   \n",
       "1                 39.0                39.5                            39.0   \n",
       "2                 51.0                52.0                            63.0   \n",
       "3  39.0 (23.75, 52.75)  39.5 (23.0, 52.75)               39.0 (32.0, 45.0)   \n",
       "4   51.0 (43.0, 62.75)   52.0 (43.0, 65.5)               63.0 (58.0, 66.0)   \n",
       "\n",
       "  ROSC, pre&post meds, Not ROSC, witnessed, pre&post meds  \\\n",
       "0                       33                             23   \n",
       "1                     40.0                           39.0   \n",
       "2                     51.0                           52.0   \n",
       "3        40.0 (22.0, 53.0)              39.0 (28.0, 52.5)   \n",
       "4        51.0 (41.0, 64.0)              52.0 (43.5, 63.5)   \n",
       "\n",
       "  ROSC, Witnessed, pre&post meds, CPC 1 or 2  \\\n",
       "0                                          5   \n",
       "1                                       39.0   \n",
       "2                                       63.0   \n",
       "3                          39.0 (32.0, 45.0)   \n",
       "4                          63.0 (58.0, 66.0)   \n",
       "\n",
       "  ROSC, Witnessed, pre&post meds, Not  \n",
       "0                                  18  \n",
       "1                                39.0  \n",
       "2                                51.0  \n",
       "3                 39.0 (23.75, 52.75)  \n",
       "4                  51.0 (43.0, 62.75)  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medians_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d7a8fa-dbc0-4edb-b556-7d027fbc72f5",
   "metadata": {},
   "source": [
    "## For Shorter post-ROSC intervals - (0,2) min and (0,3) min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "badb5e46-4525-44d4-9818-f2dee11e06a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Characteristic</th>\n",
       "      <th>All ROSC Cases</th>\n",
       "      <th>ROSC, CPC 1 or 2</th>\n",
       "      <th>ROSC, Not</th>\n",
       "      <th>ROSC, All Witnessed</th>\n",
       "      <th>ROSC, Witnessed, CPC 1 or 2</th>\n",
       "      <th>ROSC, Witnessed, Not</th>\n",
       "      <th>ROSC, pre&amp;post meds</th>\n",
       "      <th>ROSC, pre&amp;post meds, CPC 1 or 2</th>\n",
       "      <th>ROSC, pre&amp;post meds, Not</th>\n",
       "      <th>ROSC, witnessed, pre&amp;post meds</th>\n",
       "      <th>ROSC, Witnessed, pre&amp;post meds, CPC 1 or 2</th>\n",
       "      <th>ROSC, Witnessed, pre&amp;post meds, Not</th>\n",
       "      <th>pre&amp;post (0,2)</th>\n",
       "      <th>pre&amp;post (0,2), CPC 1 or 2</th>\n",
       "      <th>pre&amp;post (0,2), Not</th>\n",
       "      <th>pre&amp;post (0,3)</th>\n",
       "      <th>pre&amp;post (0,3), CPC 1 or 2</th>\n",
       "      <th>pre&amp;post (0,3), Not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>n</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pre-ROSC Median</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Post-ROSC Median</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>48.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre-ROSC Median (IQR)</td>\n",
       "      <td>39.5 (23.0, 52.75)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>40.0 (22.0, 53.0)</td>\n",
       "      <td>39.0 (28.0, 52.5)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>39.0 (23.75, 52.75)</td>\n",
       "      <td>39.5 (23.0, 52.75)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>40.0 (22.0, 53.0)</td>\n",
       "      <td>39.0 (28.0, 52.5)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>39.0 (23.75, 52.75)</td>\n",
       "      <td>39.5 (23.0, 52.75)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>40.0 (22.0, 53.0)</td>\n",
       "      <td>39.5 (23.0, 52.75)</td>\n",
       "      <td>39.0 (32.0, 45.0)</td>\n",
       "      <td>40.0 (22.0, 53.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Post-ROSC Median (IQR)</td>\n",
       "      <td>52.0 (41.75, 66.75)</td>\n",
       "      <td>63.0 (58.0, 73.0)</td>\n",
       "      <td>51.0 (38.5, 64.0)</td>\n",
       "      <td>54.0 (43.25, 63.75)</td>\n",
       "      <td>63.0 (57.5, 67.75)</td>\n",
       "      <td>51.0 (43.0, 62.75)</td>\n",
       "      <td>52.0 (43.0, 65.5)</td>\n",
       "      <td>63.0 (58.0, 66.0)</td>\n",
       "      <td>51.0 (41.0, 64.0)</td>\n",
       "      <td>52.0 (43.5, 63.5)</td>\n",
       "      <td>63.0 (58.0, 66.0)</td>\n",
       "      <td>51.0 (43.0, 62.75)</td>\n",
       "      <td>52.0 (37.0, 64.0)</td>\n",
       "      <td>62.5 (62.0, 63.25)</td>\n",
       "      <td>48.0 (37.0, 66.0)</td>\n",
       "      <td>51.0 (37.0, 65.0)</td>\n",
       "      <td>62.5 (62.0, 64.25)</td>\n",
       "      <td>46.0 (37.0, 65.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Characteristic       All ROSC Cases   ROSC, CPC 1 or 2  \\\n",
       "0                       n                   51                  9   \n",
       "1         Pre-ROSC Median                 39.5               39.0   \n",
       "2        Post-ROSC Median                 52.0               63.0   \n",
       "3   Pre-ROSC Median (IQR)   39.5 (23.0, 52.75)  39.0 (32.0, 45.0)   \n",
       "4  Post-ROSC Median (IQR)  52.0 (41.75, 66.75)  63.0 (58.0, 73.0)   \n",
       "\n",
       "           ROSC, Not  ROSC, All Witnessed ROSC, Witnessed, CPC 1 or 2  \\\n",
       "0                 42                   31                           8   \n",
       "1               40.0                 39.0                        39.0   \n",
       "2               51.0                 54.0                        63.0   \n",
       "3  40.0 (22.0, 53.0)    39.0 (28.0, 52.5)           39.0 (32.0, 45.0)   \n",
       "4  51.0 (38.5, 64.0)  54.0 (43.25, 63.75)          63.0 (57.5, 67.75)   \n",
       "\n",
       "  ROSC, Witnessed, Not ROSC, pre&post meds ROSC, pre&post meds, CPC 1 or 2  \\\n",
       "0                   23                  38                               5   \n",
       "1                 39.0                39.5                            39.0   \n",
       "2                 51.0                52.0                            63.0   \n",
       "3  39.0 (23.75, 52.75)  39.5 (23.0, 52.75)               39.0 (32.0, 45.0)   \n",
       "4   51.0 (43.0, 62.75)   52.0 (43.0, 65.5)               63.0 (58.0, 66.0)   \n",
       "\n",
       "  ROSC, pre&post meds, Not ROSC, witnessed, pre&post meds  \\\n",
       "0                       33                             23   \n",
       "1                     40.0                           39.0   \n",
       "2                     51.0                           52.0   \n",
       "3        40.0 (22.0, 53.0)              39.0 (28.0, 52.5)   \n",
       "4        51.0 (41.0, 64.0)              52.0 (43.5, 63.5)   \n",
       "\n",
       "  ROSC, Witnessed, pre&post meds, CPC 1 or 2  \\\n",
       "0                                          5   \n",
       "1                                       39.0   \n",
       "2                                       63.0   \n",
       "3                          39.0 (32.0, 45.0)   \n",
       "4                          63.0 (58.0, 66.0)   \n",
       "\n",
       "  ROSC, Witnessed, pre&post meds, Not      pre&post (0,2)  \\\n",
       "0                                  18                  38   \n",
       "1                                39.0                39.5   \n",
       "2                                51.0                52.0   \n",
       "3                 39.0 (23.75, 52.75)  39.5 (23.0, 52.75)   \n",
       "4                  51.0 (43.0, 62.75)   52.0 (37.0, 64.0)   \n",
       "\n",
       "  pre&post (0,2), CPC 1 or 2 pre&post (0,2), Not      pre&post (0,3)  \\\n",
       "0                          5                  33                  38   \n",
       "1                       39.0                40.0                39.5   \n",
       "2                       62.5                48.0                51.0   \n",
       "3          39.0 (32.0, 45.0)   40.0 (22.0, 53.0)  39.5 (23.0, 52.75)   \n",
       "4         62.5 (62.0, 63.25)   48.0 (37.0, 66.0)   51.0 (37.0, 65.0)   \n",
       "\n",
       "  pre&post (0,3), CPC 1 or 2 pre&post (0,3), Not  \n",
       "0                          5                  33  \n",
       "1                       39.0                40.0  \n",
       "2                       62.5                46.0  \n",
       "3          39.0 (32.0, 45.0)   40.0 (22.0, 53.0)  \n",
       "4         62.5 (62.0, 64.25)   46.0 (37.0, 65.0)  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cases with pre- AND post-ROSC medians, post-ROSC interval (0,2) min\n",
    "dfs = [dfroscdiff, dfroscdiffcpc12, dfroscdiffnot]\n",
    "colnames = ['pre&post (0,2)', 'pre&post (0,2), CPC 1 or 2', 'pre&post (0,2), Not']\n",
    "\n",
    "for i in range(len(dfs)):\n",
    "    d = dfs[i]\n",
    "    col = []\n",
    "    col.append(len(d))\n",
    "    col.append(d['Pre-ROSC Medians (-5,-2)'].median())\n",
    "    col.append(d['Post-ROSC Medians (0,2)'].median())\n",
    "    pre = str(d['Pre-ROSC Medians (-5,-2)'].median()) + ' (' + str(d['Pre-ROSC Medians (-5,-2)'].quantile(0.25)) + ', ' + str(d['Pre-ROSC Medians (-5,-2)'].quantile(0.75)) + ')'\n",
    "    col.append(pre)\n",
    "    post = str(d['Post-ROSC Medians (0,2)'].median()) + ' (' +str(d['Post-ROSC Medians (0,2)'].quantile(0.25)) + ', ' + str(d['Post-ROSC Medians (0,2)'].quantile(0.75)) + ')'\n",
    "    col.append(post)\n",
    "    medians_df[colnames[i]] = col\n",
    "\n",
    "\n",
    "# Cases with pre- AND post-ROSC medians, post-ROSC interval (0,3) min\n",
    "colnames = ['pre&post (0,3)', 'pre&post (0,3), CPC 1 or 2', 'pre&post (0,3), Not']\n",
    "\n",
    "for i in range(len(dfs)):\n",
    "    d = dfs[i]\n",
    "    col = []\n",
    "    col.append(len(d))\n",
    "    col.append(d['Pre-ROSC Medians (-5,-2)'].median())\n",
    "    col.append(d['Post-ROSC Medians (0,3)'].median())\n",
    "    pre = str(d['Pre-ROSC Medians (-5,-2)'].median()) + ' (' + str(d['Pre-ROSC Medians (-5,-2)'].quantile(0.25)) + ', ' + str(d['Pre-ROSC Medians (-5,-2)'].quantile(0.75)) + ')'\n",
    "    col.append(pre)\n",
    "    post = str(d['Post-ROSC Medians (0,3)'].median()) + ' (' +str(d['Post-ROSC Medians (0,3)'].quantile(0.25)) + ', ' + str(d['Post-ROSC Medians (0,3)'].quantile(0.75)) + ')'\n",
    "    col.append(post)\n",
    "    medians_df[colnames[i]] = col\n",
    "\n",
    "medians_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c638be-8e39-468e-ba9c-9ebed35bbf57",
   "metadata": {},
   "source": [
    "## Diffs Table per interval group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8c0a07bd-cbaa-4f45-a814-3481df2540f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CASSID',\n",
       " 'ROSC-CPC12',\n",
       " 'Witnessed',\n",
       " 'Pre-ROSC Medians (-5,-2)',\n",
       " 'Post-ROSC Medians (0,5)',\n",
       " 'Pre-ROSC Lowers (-5,-2)',\n",
       " 'Pre-ROSC Uppers (-5,-2)',\n",
       " 'Post-ROSC Lowers (0,5)',\n",
       " 'Post-ROSC Uppers (0,5)',\n",
       " 'Pre-ROSC Vals (-5,-2) (rSO2 %)',\n",
       " 'Post-ROSC Vals (0,5) (rSO2 %)',\n",
       " 'Post-ROSC Medians (0,2)',\n",
       " 'Post-ROSC Lowers (0,2)',\n",
       " 'Post-ROSC Uppers (0,2)',\n",
       " 'Difference (0,2) (rSO2 %)',\n",
       " 'Post-ROSC Vals (0,2) (rSO2 %)',\n",
       " 'Post-ROSC Medians (0,3)',\n",
       " 'Post-ROSC Lowers (0,3)',\n",
       " 'Post-ROSC Uppers (0,3)',\n",
       " 'Difference (0,3) (rSO2 %)',\n",
       " 'Post-ROSC Vals (0,3) (rSO2 %)',\n",
       " 'Difference (0,5) (rSO2 %)']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dfroscdiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "879c7288-b5a3-4189-ab2c-366a9401c558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post-ROSC interval</th>\n",
       "      <th>Overall, median</th>\n",
       "      <th>CPC 1 or 2, median</th>\n",
       "      <th>Not, median</th>\n",
       "      <th>Overall, upper diff</th>\n",
       "      <th>CPC 1 or 2, upper diff</th>\n",
       "      <th>Not, upper diff</th>\n",
       "      <th>Overall, lower diff</th>\n",
       "      <th>CPC 1 or 2, lower diff</th>\n",
       "      <th>Not, lower diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(0,2) min</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0,3) min</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.25</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.75</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0,5) min</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  post-ROSC interval  Overall, median  CPC 1 or 2, median  Not, median  \\\n",
       "0          (0,2) min              9.0                24.5          8.0   \n",
       "1          (0,3) min             10.0                26.0          9.0   \n",
       "2          (0,5) min             11.0                29.0         10.0   \n",
       "\n",
       "   Overall, upper diff  CPC 1 or 2, upper diff  Not, upper diff  \\\n",
       "0                 9.00                    8.50              8.0   \n",
       "1                 9.00                    9.25              9.0   \n",
       "2                 9.75                    5.00              9.0   \n",
       "\n",
       "   Overall, lower diff  CPC 1 or 2, lower diff  Not, lower diff  \n",
       "0                  5.0                   10.50              5.0  \n",
       "1                  6.0                   11.75              5.0  \n",
       "2                  5.0                   16.00              6.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = ['(0,2) min', '(0,3) min', '(0,5) min']\n",
    "diffs_df = pd.DataFrame(groups, columns=['post-ROSC interval'])\n",
    "\n",
    "dfs = [dfroscdiff, dfroscdiffcpc12, dfroscdiffnot]\n",
    "colnames = ['Overall, median', 'CPC 1 or 2, median', 'Not, median']\n",
    "upnames = ['Overall, upper diff', 'CPC 1 or 2, upper diff', 'Not, upper diff']\n",
    "lownames = ['Overall, lower diff', 'CPC 1 or 2, lower diff', 'Not, lower diff']\n",
    "\n",
    "# Medians\n",
    "for i in range(len(dfs)):\n",
    "    col = []\n",
    "    d = dfs[i]\n",
    "    col.append(d['Difference (0,2) (rSO2 %)'].median())\n",
    "    col.append(d['Difference (0,3) (rSO2 %)'].median())\n",
    "    col.append(d['Difference (0,5) (rSO2 %)'].median())\n",
    "    diffs_df[colnames[i]] = col\n",
    "\n",
    "# Uppers diffs\n",
    "for i in range(len(dfs)):\n",
    "    col = []\n",
    "    d = dfs[i]\n",
    "    col.append(d['Difference (0,2) (rSO2 %)'].quantile(0.75) - d['Difference (0,2) (rSO2 %)'].median())\n",
    "    col.append(d['Difference (0,3) (rSO2 %)'].quantile(0.75) - d['Difference (0,3) (rSO2 %)'].median())\n",
    "    col.append(d['Difference (0,5) (rSO2 %)'].quantile(0.75) - d['Difference (0,5) (rSO2 %)'].median())\n",
    "    diffs_df[upnames[i]] = col\n",
    "\n",
    "# Lowers diffs\n",
    "for i in range(len(dfs)):\n",
    "    col = []\n",
    "    d = dfs[i]\n",
    "    col.append(d['Difference (0,2) (rSO2 %)'].median() - d['Difference (0,2) (rSO2 %)'].quantile(0.25))\n",
    "    col.append(d['Difference (0,3) (rSO2 %)'].median() - d['Difference (0,3) (rSO2 %)'].quantile(0.25))\n",
    "    col.append(d['Difference (0,5) (rSO2 %)'].median() - d['Difference (0,5) (rSO2 %)'].quantile(0.25))\n",
    "    diffs_df[lownames[i]] = col\n",
    "\n",
    "diffs_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deea1918-bc65-4d21-a576-5b1b2ada1517",
   "metadata": {},
   "source": [
    "# 4 - Statistical Tests\n",
    "1. Take median rSO2 for [-5, -2] and for [0, 5] with t=0 at time of ROSC  \n",
    "2. Find difference between those values per case"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca5d3e3-568c-45a5-80bb-9282d001d616",
   "metadata": {},
   "source": [
    "## Wilcoxon Signed-rank test\n",
    "for matched, dependent samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "70ffd212-db45-4dd1-8f3e-12d167c354ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import wilcoxon as wilcox"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f891938-ee1c-48a9-b395-373f8faf13a3",
   "metadata": {},
   "source": [
    "### All ROSC cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b1f3d789-c8b0-4128-bfaf-d795c150cad1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.5 0.0012783504538049716 18\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC'] == 1) & (df['Witnessed'] == 1) & (df['ROSC-CPC12'] == 0) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue, len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "93f65624-d36e-4937-8e20-3b6c4554a7d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.5 9.372836510915372e-07\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue)\n",
    "\n",
    "# # Calculate the maximum statistic\n",
    "# maxwilcox = len(x) * (len(x) + 1) * 0.25\n",
    "# aucwilcox = round(res.statistic / maxwilcox, 4)\n",
    "# print('AUC: ' + str(aucwilcox))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6848640a-7f00-4fa4-a922-b64971e6c678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "957a4068-c00f-412e-8d21-6f4cd013188c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3dac76b8-8083-46fb-adcb-ac7124154456",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot differences histograms\n",
    "sns.histplot(x, kde=False, color='blue', label='Difference in pre- and post-ROSC medians', alpha=0.5, bins=10)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('Difference in pre- and post-ROSC medians per case', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3057d14-88b6-4864-95ef-90ef03560908",
   "metadata": {},
   "source": [
    "### ROSC - Survived with CPC 1 or 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "87d5dfde-c616-4e8e-8310-cdce2f8228b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0625, 24.2, 29.0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xcpc12 = df[(df['ROSC-CPC12'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)'].tolist()\n",
    "res = wilcox(xcpc12)\n",
    "res.statistic, res.pvalue, np.mean(xcpc12), np.median(xcpc12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "72169698-7635-44b4-934d-f0c741144a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0625, 24.2, 29.0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xcpc12 = df[(df['ROSC-CPC12'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)'].tolist()\n",
    "res = wilcox(xcpc12)\n",
    "res.statistic, res.pvalue, np.mean(xcpc12), np.median(xcpc12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cba8dee-ee53-49ce-aaaf-f306d636122e",
   "metadata": {},
   "source": [
    "### ROSC - Did not survive with CPC 1 or 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d6f6daeb-74e3-4be6-a9b5-b8c76b496062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25.5, 8.148019104134807e-06, 11.878787878787879, 10.0, 33)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xnot = df[(df['ROSC-CPC12'] == 0) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)'].tolist()\n",
    "res = wilcox(xnot)\n",
    "res.statistic, res.pvalue, np.mean(xnot), np.median(xnot), len(xnot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6f32e16d-7bfe-41fa-8dfd-54f8ce60a340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.5, 0.0012783504538049716, 11.055555555555555, 10.0, 18)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xnot = df[(df['ROSC-CPC12'] == 0) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)'].tolist()\n",
    "res = wilcox(xnot)\n",
    "res.statistic, res.pvalue, np.mean(xnot), np.median(xnot), len(xnot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f0cb97-f52b-43da-8759-0d63c4be637d",
   "metadata": {},
   "source": [
    "### Plot Diff in rSO2 around ROSC for CPC 1/2 vs. Not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9bfd86a4-d906-4480-8113-f2d753c89d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot overlapping histograms\n",
    "sns.histplot(xcpc12, kde=False, color='red', label='ROSC-Survive with CPC 1 or 2', alpha=0.5, bins=10)\n",
    "sns.histplot(xnot, kde=False, color='blue', label='ROSC-Not', alpha=0.5, bins=10)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('Difference in pre- and post-ROSC medians per case', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0b189c-0fba-4cf7-800b-059d5333d666",
   "metadata": {},
   "source": [
    "### Plot Pre-ROSC vs. Post-ROSC Medians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3c6de489-d05d-4a94-9ab5-e6f93f5c0e47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpre = df[(df['ROSC'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Pre-ROSC Medians (-5,-2)'].tolist()\n",
    "xpost = df[(df['ROSC'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Post-ROSC Medians (0,5)'].tolist()\n",
    "\n",
    "# Plot overlapping histograms\n",
    "sns.histplot(xpre, kde=False, color='red', label='Pre-ROSC Medians', alpha=0.5, bins=15)\n",
    "sns.histplot(xpost, kde=False, color='blue', label='Post-ROSC Medians', alpha=0.5, bins=15)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('Pre- and Post-ROSC medians per case, (0,5) min interval', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fdcd0f5-7bbd-4782-bb4c-252e47701f7d",
   "metadata": {},
   "source": [
    "### Plot Pre- vs. Post- for Outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ce83848b-8b60-4dca-9cf8-172a0cd4c5f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpre = df[(df['ROSC-CPC12'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Pre-ROSC Medians (-5,-2)'].tolist()\n",
    "xpost = df[(df['ROSC-CPC12'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Post-ROSC Medians (0,5)'].tolist()\n",
    "\n",
    "# Plot overlapping histograms\n",
    "sns.histplot(xpre, kde=False, color='red', label='Pre-ROSC Medians', alpha=0.5, bins=3)\n",
    "sns.histplot(xpost, kde=False, color='blue', label='Post-ROSC Medians', alpha=0.5, bins=3)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('Survival with CPC 1 or 2, (0,5) min interval', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "60e8550e-4857-4fc4-9f12-4b09420ec4ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xs7y5HhheuXLlQmOcM1EvjgeVF1cdnz59Kg1QAWS3K0OSmJzDr+c3UqFOQUnZ8zp16iQlXr///js+++yzXHFJT0/Xe+xAYYOw/Pnnn5g7d26hPXM5PX36lIkXUTFi4kVEJcIHH3yAI0eOAMi+uXzIkCEGJ0K6ASGA7ITB29vboOXKli0rJV5Pnz4tYo1Lrucv68rIyDAo8crZy5VXT5WlREZGSolXUlISzp49W+yDAOR374xOzhEUdfcLGlperVYbX7EcDNluzt5Oc223KIqrjs/3gP7www9FXkdhD+YuytD3NWrUQPXq1XHt2jUkJydj//79eve2AcDevXulS3xr1apV4PFv/vz5es8ZNNTzlyETkXXxHi8iKhHq1KmD5s2bA8gePn3evHkGL5vzZMLJycng5XImFyX5hCTnPhlyCeDzlyPmvFSxIDnLGXIJlbmUL18eZcuWld7nHAq/IDmfyVSUv7shijLqoDlGKDRGcW23KIqrjrrL60yhuz8sP0XtFc452E/OSwp1co5mWFBv15EjR/SSrldffRVTpkzBli1bcOzYMVy4cAH//POP9C/nM/oKGtiDiCyPPV5EVGKMHj0a+/fvhyiK+P3333HlyhUEBgYWulzOHp6iXHKWcwCJgm7+L26+vr7Sa0N65ry9vaFUKqVf7O/fv19o72FmZqZez6Ehoyeak5+fn3R/z/OjMuYnZ31zxogo548qSqUSJ0+eLMbaZHvzzTcxZ84caLVaHDhwAElJSdJjLJ48eYKjR48CyO4dLeietKioKOn1W2+9hS+++KLA7ZbkH5WIXjbs8SKiEqNGjRpo27YtgOzn0MydO9eg5XJe8pORkaF3Ql6Q+/fvS69zDtRQ0uTsDTJ0uP2cidaVK1cKLX/p0iXptVwuR+XKlYtQQ9PlTIIN7b2KjY2VXpcrV87sdSLblfNy46SkJIOPCZZUunRpvRFcd+3aJc3bvn27dJlleHh4vj8kaDQaaVREmUyGDz74oNDt5jzOEVHxYuJFRCVKZGSkdD/M/v37ce7cuUKXKVWqlN6J1pkzZwpd5unTp7h586b0PufIajol5VKunA9czVnngjRo0EB6nXPEx/zk7BEICQkp0sOMTZWRkaE3EIKhvVe6+/MAmPXhyWT7/Pz89B4crhupsLjlvIQw5+WGz49mmJ+nT58iKysLQHZyWdh9Zjdu3Hih7l8lsnVMvIioRKlSpYreycm3335r0HI5E43NmzcXWn7Lli3S/Q5+fn55XlqXM/kojoEJdCpXriwNUpCQkKDX05OfnM8x++uvvwrtKcsZs5YtWxpZU+Ns3bpVul9LEASEhoYWuszTp0+lBy47OzsXOgoh2b6cA8Toko+CNGnSRHq9evXqEvGsvrZt20r7cfLkSTx48AC3bt2SfmBycnJCq1at8l0+58OfMzIyCt2n9evXm6HWRGQuTLyIqMQZMWKENNLZ0aNHDbo/46233pJe79mzBwcPHsy37IMHD/Ddd9/pLZtX71bOyw8NSXYsRSaT6T335/Tp04UuU6dOHQQFBQHIvjxp5syZ+ZbdsGGD1JPm4uKCTp06mVRflUqV61li+bl9+zZmzZolvX/99dfh4+NT6HI5YxAWFqY3aiC9mHIOGmPI5/Hdd9+V2sXFixeLNAqguZ8Lp+Pm5iY910wURWzdulWvt6tFixYF3m/q4eEhjbiZnJxc4MOYT58+zcSLqIRh4kVEJU758uXRrVs36f3Zs2cLXSYsLAxvvPGG9H706NHYuXNnrnKXL1/GO++8g8TERADAK6+8gv79++e5zurVq0uvDx8+bPBIaf369UNAQAACAgIwfvx4g5YpTLNmzaTXx44dM2iZsWPHSq+3bt2KWbNm5eop2LFjB6ZOnSq9f/fddwu8fGnTpk3SvgUEBORZJi4uDq1atUJUVFS+z0LSarXYvn073nrrLelSKDs7O3z44YcG7VvOGOSMDb24cn4e8/psP69ChQoYNmyY9H7BggUYP358vr2/Go0GR48exSeffIIuXbqYXuF8PH+5Yc7Eq7Bnd8lkMr2evAkTJuD8+fO5yu3YsQNDhgyBRqOx6qMhiKhgHNWQiEqk4cOHY/PmzXpDhhdm2rRp6N27N27fvo20tDR88MEH+Pbbb1GnTh3Y2dkhJiYGZ8+elS7PcXZ2xqxZs6SRxZ4XFBSEMmXK4P79+4iPj0e7du3QsGFDeHp6Sj1kQUFBaN++vek7XIjWrVtj8uTJyMzMxB9//IHPP/9c77KjvISHh2PYsGFS797SpUuxZcsWhIaGwt7eHpcuXcK1a9ek8g0bNsTQoUPNUt+HDx/i66+/xjfffINy5cqhWrVq8PDwgJ2dHeLj43Hu3Dm9h17LZDJMmTIFtWvXNmj9+/btA5CdrD3/PCR6MbVp0waHDh0CAMyaNQuHDh1CtWrV9C4JHjp0qN6jEEaOHIl79+5Jl9Ju3rwZv/32GwIDA1GlShU4OzsjJSUF9+/fx9WrV6VBXp5/JIM5NWnSBB4eHkhISND7/Hl7e6Nhw4aFLj98+HDs27cPGRkZuHfvHnr27Im6deuicuXKUKlUOHv2LO7evQsA6NmzJ/79998Ce8aIyHqYeBFRiVSqVCn06tULq1evNngZHx8frF+/HmPHjpV6RP7991+9gRt0KlasiJkzZ6JOnTr5rk8mk+Hzzz/HyJEjoVKpEB8fjy1btuiV6dKlS67EK+d9F4UlR4Zyc3NDmzZt8NtvvyE+Ph4nT57Uu68tP6NHj4a9vT0WLVqErKwsxMXFYfv27bnKdejQAV988QUUioK/Foq6b6Io4s6dO7hz506+ZcqXL4+pU6fqPW+oIOfOnZNGamvVqpVVnzlGxadLly7YunUrjh8/DlEUcfz48VwDx/Tp00evPQiCgOnTp6N27dqYN28eEhMTodFocPHiRVy8eDHP7QiCgHr16llsP+zs7NC2bVv8+OOPetPbt29f6OcPAKpWrYpZs2bho48+Qnp6OkRRxJkzZ3INKvTWW2/hf//7HwYNGmTW+hOR8Zh4EVGJ9f7772Pjxo16Q40XxsfHB6tXr8ahQ4ewY8cOnD59GvHx8VCr1fD29kZgYCBatmyJN998U7qPrCBNmjTBpk2bsG7dOvz999+4d+8e0tLS8r2pXRRFXL9+XXofERFhcN0LM2DAAOkhqxs3bjQo8RIEAcOHD0ebNm2wceNGHDlyBA8ePIBarYavry/q1q2LLl264PXXXzeoDjl/oc/vWUNly5bF1q1bcebMGZw9exbXr1/HkydPkJCQAJVKBRcXF/j5+SE4OBjNmjVDs2bNipSg/vTTT9Lr/C4TpRePQqFAVFQUfvnlF+zevRvXrl1DQkKCQQNt9O3bF507d8avv/6Ko0eP4urVq3jy5InUHkuVKoVq1arhtddeQ5MmTfDKK69YdF86deqUK/Eq7DLDnFq2bImtW7di1apVOHz4MB48eAC5XA4/Pz/Uq1cPXbt2NWiQGiKyLkEsCcP8EBG9IK5evSoNTvHqq6/ihx9+MOv6Bw4ciKNHj8Le3h779+83aCAKc+rcuTOuXLkCuVyObdu2Wf1By4mJiWjatCnS0tJQv359fP/991bdPhERkbE4uAYRkRnlHPRh1KhRZl//6NGjAWSPHLhmzRqzr78gCQkJuHr1KoDsnjxrJ10AsHbtWqkH1JCHxxIREZUUTLyIiMxIl3iFhoYiPDzc7OuvW7euNJjE999/b9WHo544cQKiKEIul2P48OFW265OcnIy1q5dCwBo3rw5L6UiIiKbwsSLiMhMNBoNTp06BcAyvV0648ePh5OTE1JSUvSeR2ZpuoEM3nzzTVSqVMlq29VZunQpEhISYG9vj4kTJ1p9+0RERKbgPV5EREREREQWxh4vIiIiIiIiC2PiRUREREREZGFMvIiIiIiIiCyMD1A2giiK0GpNuzVOJhNMXgcZjvG2Hsbauhhv62GsrYvxth7G2roYb+uxRqxlMgGCIBhUlomXEbRaEU+epBq9vEIhg6enC5KS0qBWa81YM8oL4209jLV1Md7Ww1hbF+NtPYy1dTHe1mOtWHt5uUAuNyzx4qWGREREREREFsbEi4iIiIiIyMKYeBEREREREVkYEy8iIiIiIiILY+JFRERERERkYRzVkIiIiOglpdVqkJGRAZUqExoNhzi3NK1WQEaGnPG2AnPEWi5XQCYzXz8VEy8iIiKil4woikhKeoL09BTIZDJotRza3FoePWK8rcUcsXZycoVS6WXws7oKYnOJV2pqKtq1a4fY2Fj8/PPPCAoKKrD85s2bsWTJEty7dw8VK1bEiBEj0K5dOyvVloiIiKjk0SVdrq6ecHZ2hEZT3DV6ecjlAnu7rMSUWIuiCJUqEykpTwEA7u7eJtfH5hKvRYsWQWPg0WHXrl0YP348hgwZgoYNG2Lv3r0YM2YM3Nzc0KhRIwvXlIiIiKjk0Wo1UtLl6qqEQiHjw3ytiPG2HlNjbW/vAABISXkKNzdPky87tKnBNaKjo/HDDz9g1KhRBpWfO3cu2rZti7FjxyIsLAz/+9//0LBhQ8ybN8/CNSUiIiIqmXQ/YOtOKokof7rPiUajNnldNpV4ffXVV+jVqxcqV65caNk7d+4gJiYGERERetMjIiJw/vx5PHnyxFLVJCIiIirxzHHPCtGLzpyfE5tJvHbt2oWrV69ixIgRBpWPiYkBAFSpUkVvur+/P0RRlOYTERERERFZmk3c45Weno7p06fjww8/hKurq0HLJCYmAgCUSqXedHd3d735xlIojM9Z5XKZ3v9kWYy3+QiCAJks/19+dPPs7OSMdw5arQhRNP+N1Gzb1mOtWBf2GXtZ8FjyH0scP7Ta/9qY7sd8QQBEMTv2xdUGtVoRWm3R9jUqaglWrlwmvffw8IC/fzW8++4QBAeHmLuK+W7X3d0dFSpUQv/+AxEennscgZSUFKxbtwoHDvyBhw8fwNnZGcHB9dC//7uoUSNQr2xGRgbWr1+Lfft248GD+3B0dMQrr5RFaGgDvP++fgdEYmICvv9+DQ4d+hOxsQ+hUNjB378qOnfuhhYtWkOhyPtUf8eOrZg6dTLs7e3x22+7c51fz5w5HVu2/IzKlatg7dqfjAtSDlevXsZ77/XHvHmLUa9efQBA9+4d8frrjfDhh+NMXn9enm/bppLLBZPO/wEbSby+++47eHt7o2vXrkVe9vnuQd3By5RuQ5lMgKeni9HL6yiVTiavgwzHeJuBVgsYcGOpq6ujFSpjQwyMm7HYtq3H0rG2cFOxKVotjyWAZdpERoYcjx7JIJcLej8qCIIAdzdHCMWU7IoaLRKTM4qUaMpkAhwcHLFw4WIAQFxcHFauXI7Ro4dh1arvUbVqNYvU9fntxsfHY+3aVRg37kMsXhyFOnWCpbJPnjzBsGHv4enTJxgw4F0EBtbC48ePsHHjBgwdOhBffTUDTZo0k8r/738f4/Llyxgw4F0EBAQgKSkZV65cwuHDBzBixH/jHNy7dxcjRgyBSpWF3r37oGbNWsjKysKZM6fx7bffABDRoUPHfOsPAAqFAocO/YGOHTtL89RqNf78cy+cnZ0hCKYnG4D+j1e69c2YMQtubkqzrN+QbRtLqxUgk8ng7u4MR0fTjkklPvG6d+8eVqxYgYULFyIlJQUAkJaWJv2fmpoKF5fcSVDOni0fHx9pelJSEoDcPWFFodWKSEpKM3p5uVwGpdIJSUnp0Gg4qo2lMd7moYujav0GaOPi8ywjkwmwt1dApVIX+VfLF5XMzxf2vd+ySPtj27Yea8Rat43169WIi3u5Pz+BgQJat1ZgwwYNHj58edu2n5+A3r0VZm93KlUmtFotNBoRGo0WcrlM+l+Qy5DxwwaIcXFm254hBD8/OL79FkRRLNIodFqtCJlMQI0atQEANWoA1avXRI8eHbFp08959qaIooisrCzY29sbXd+8thsYGIQuXdph27bfULPmf487+vrrqbh37y6iotahatWqUrwbN26GyMih+OKLSVi/fhO8vLxx585tHDv2Fz799HO0a/ffOAVvvNEMgwcP14vNpEmfIjNTheXL16BUqdLS9Pr1w9Cly1t49Cgu31jqvqMbNWqCXbt2oV27N6V5f/11FOnp6QgPb4hbt/41ywiMuvar0Wil9fn7VwcAi43wKAiQYm1Kj5dGI0Kr1SIxMQ3p6blHVlcqnQxO7kp84nX37l1kZWVhyJAhueb1798fwcHB+Omn3F2gunu7YmJi4O/vL02Pjo6GIAi57v0qKnM1Qg4naj2Mt3loYuOgvXc/z3miTACc7KFJVzHxekb3y60l2x/btvVYI9axsVrcu/dyf358fbNPYuLitLh79+Vt26KY3Sth7naX87lGuhPSnCemYlz+x3lLMWefR+nSpeHu7oEHD7L34auvPsfVq5cxfHgkFi9eiFu3bmLSpClo3rwlLl48j6VLF+Hy5YuQy+UID2+E0aPHwtPTq8jb9fHxgYeHJ2JjY6VpDx8+xIED+9GuXQT8/avqxVuhUGDw4GEYNep9bN26BQMGDEJKSrK0ruflHMr83LmzuHTpAkaP/kgv6cpZl7zW8bxWrdpg3LgP8fjxI3h7Z5ffs+d3NGz4Bhwcco96mZycjCVLFuLQof1ISkpC5cr+GDp0JF57LUyv3KpVy7Fp00akp6chNDQMHTt2yrWu5y81vHjxPNauXYmrV68gNTUF5cpVQK9efdC2bQdpmb//PoXIyKGYPXsBduzYiiNHDkGpVKJr1x7o02eAVC4mJhqLFs3F5cuXkJmZAT+/UoiI6KRXpqg0mqL9KJCXEp94BQYGYs2aNXrTrly5gmnTpmHy5Mn5PkC5fPnyqFKlCnbs2IFWrVpJ07dt24Y6derAy6voHygiIiIiKtlSU1OQnJwEHx9fadqjR48wd+4sDBgwCH5+peDnVwoXL57HqFHvIyysISZPnoaMjHQsW/Ydxo37EEuXrirydtPS0pCUlIiyZctK086ePQ1RFPHGG03zXCYk5FW4urrhzJnTGDBgECpUqAgnJ2csWPAthgwZgZCQV+Hs7JxruTNnTgEAwsMbFrmeOQUG1kaZMmWxb98e9OzZGxkZGTh06AAmTfoCBw/+qVc2KysLY8aMwJMnjzF48HD4+vph9+4d+Pjj0Vix4nv4+1cFAPzyywYsX74YvXv3Q/36r+HEiWP4+uuphdbl4cMHCAoKRufO3WBv74ALF85h+vQvIYqiXu8fAMycOQ1t2rTH1Knf4MCB/fjuu/nw96+GsLDXAQDjx38IT08vfPrpJDg5ueDu3TuIj7duL25eSnzipVQq0aBBgzzn1apVC7Vq1QIATJw4EVu2bMHly5el+ZGRkRgzZgwqVKiA119/Hfv27cORI0ewfPlyq9SdiIiIiCxPrc5+xlJ8fBwWLJgDjUaDpk1bSPOTk5Mwa9Y81KxZW5o2Y8YU1KgRiKlTv5Hu/a9c2R8DBvTCX38dznOQjPy2+/jxIyxevAAuLq7o0aO3ND8+PvvS/FKlXsl3HaVLvyIlBS4urhg//v8wY8YUjBs3BnK5HP7+1dCkSTP07Pk2nJyy7zN99Ch7vX5+pQoPTiFatmyDPXt2oWfP3jh06E8oFAqEhTXMlXjt3r0T16//g1Wr1qNy5ewrxxo0CMft27exatVyfPnldGg0Gqxduwpt2rTHiBGjpTKPHz/C3r2/F1oPHVEUERwcgri4WPz666ZciVfTps0xaND7AIBXXw3F0aOH8Oef+xAW9joSEhJw//49REaORZMmTaFWa6UBPYpbiU+8DJV9rbL+dZft2rVDRkYGFi9ejKioKFSsWBFz5sxBo0aFf5CIiIiIqORLT09H06b/Xerm5qbEmDGfoEGDcGmah4eHXtKVkZGBCxfOYcSI0XrnjxUqVIS3tw+uXLmM8PBG0Gg0eoN9yOVyKUl7frtyuRzTp89C+fIVirwPOQd9a9GiFUJDG+Do0UP4++9TOH36JJYt+w67d+/E8uVr4eTkZJbB4nRat26LVauW4969u9iz53c0bdoCdnZ2ucqdOHEM/v5VUb58BSnhBID69V+Tkqr4+Dg8ehSPN95oprdss2YtCk28kpKSsGLFEhw6dACPHsVLfxfduA05hYb+F3eZTIaKFSsh7tl9ie7u7ihd+hUsWbIAqanJCAmpb5YE1RxsMvFq0KAB/vnnH71p06dPx/Tp03OV7dKlC7p06WKtqhERERGRFTk4OGDhwmUABHh4eMDPr5Te/VAA4OGhf4tJcnISNBoN5s2bjXnzZudaZ1xc9n1ao0cPw9mzf0vTcw6HrtuuVqvF3bt3sHjxAnz55WdYs2aDdH+Vr2/25Y6xsQ9QrVr1POv/8OGDXEPKK5VKtG3bAW3bdoAoioiKWoJVq5Zj27Zf0aNHL/j6+j1b70OUK1fe0FDlqUKFSqhevQZ++WUDTpz4C7NnL8izXGJiAq5d+0cv2dSRy+UAsi/pBABPT0+9+YbcMzd16ue4ePE83nnnPVSu7A8XFxds3vwz/vhjT66ybm5ueu/t7OykwfcEQcDs2fOxdOl3mDlzOtLT01G9eg1ERn6IunXrFVoPS7LJxIuIiIiICMju8ahRo2aBZZ7vGHJ1dYMgCOjXb2Ce91+5u3sAAD75ZKJ0Qg9k94jltd2aNWujQoVKGDJkAFatWoaPPpoAAKhb91UIgoDDhw+iUaMmubZz7twZpKQkIyTk1QLqLuDtt/th1arluHXrJgBIyd+xY0fQvXuvAvfdEK1atcV3382Dt7dPvsmJUukOf/9qmDDh//Jdjy7hfPr0qd70p0+fFLj9zMxM/PXXEYwY8YHe/hj7DLsKFSphypQZADQ4c+Ysli5diHHjxmDz5p153jNnLUy8iIiIiOil4uTkhNq1g3Dr1k3UqDE833IVKlQyeJ01agSiZcs22LFjKwYOHAxvbx+ULl0aTZo0w65d29GzZ29UqVJVKq9Wq7Fs2XdwcXGRnqOVlpYKuVwOBwf950Xdvn0bAODl5Q0AqFOnLmrVCsLatSvRpElzqQdM58mTx4iLiy00IdVp1aoNzp8/g9DQsFy9hTr167+Gv/46Ah8fX72BS3Ly9fWDt7cPDh7cr/dssv379xW4/aysLGg0Gr1LHNPSUnH48EGD6p8fhcIOISGvok+fdzB+/Id49CheL3m2NiZeRERERPTSGT58NEaPHoZJkyagRYvWcHNzQ3x8HE6ePI727TsaNSDDO+8Mwt69v+Onn9Zj2LDshx2PHTseMTHRGDXqffTtOxC1atXCo0eP8fPPP+Ly5Yv44ovpUkJ1+/YtjBs3Bu3adUSdOnXh5OSEW7duYu3aVXB1dUX79v89EHnSpC8RGTkU773XD2+91Qc1atSEWq3G+fNnsWnTRowaNcbgxMvHxxfTps0qsEzbth3w66+bMHLk++jduy/Kl6+AlJQUXL/+D7KysjB06EjI5XL07fsO5s6dCS8vb4SGNsDx43/h3LkzBa7b1dUVgYE1sW7dKnh4eEAuV2DdulVwcXFFQkLBvWXPu3HjOhYsmIMWLVqjQoXySExMxtq1K/HKK2VQtmy5Iq3L3Jh4EREREZFE8PMz63O1DN2mtQUFBWPRouWIilqCadMmIysrC76+pVC/fqjR901VqFAJLVu2wZYtP6Nfv4FwdXWFp6cXli5djbVrV+LXX3/B0qUL4ezsjDp1QrB48Uq9+7vKli2PN9/sipMnj2Pbti1IS0uDj48vXn01FP37v4vSpV/JUbYcoqLW4YcfVuO337Zg+fLFkMsVqFq1GoYMGYZWrdqaHKOc7O3tMW/ed1ixYinWrFmBx48fwd3dA9WrB6BLlx5Sue7d30JKSjI2bdqIzZs3on791/DxxxPxyScfFLj+zz77Cl9//RW++upzKJXu6N69F9LT0/Djj+uKVE9vb294e3tj7dqVePQoHi4urggOrotJk76U7kUrLoJo7MWTLzGNRosnT1KNXl6hkMHT0wVPn6byoadWwHibhy6O6d/Oz/fBmjKZACcne6TzAcoSWdkycPpglEXaH9u29Vgj1rptfPut6qV/gHJIiAxvv22H+fOzcPv2y9u2y5YV8MEH9mZvd1lZKjx+/ADe3q/Azs4eCoUMarUWMpkALw9nCHJrp13ZRI0WTxLSXvjvD128yfLMEevnPy/P8/JygdzAzwx7vIiIiIgIWq2IJwlpkMlMH6Lc2O2/6EkXvdyYeBERERERACY/RJZUPH3JRERERERELxEmXkRERERERBbGxIuIiIiIiMjCmHgRERERERFZGBMvIiIiIiIiC2PiRUREREREZGFMvIiIiIiIiCyMiRcREREREZGF8QHKRERERAQAkMkEyGRCsWzbmIc3R0UtwcqVy6T3Hh4e8PevhnffHYLg4BCz1e3vv0/h4sXz6N//XYPKd+/eEQ8fPgAAyOVy+Pr6ISTkVbz33lCULVsmV/mrVy9jzZqVOH/+DFJTU1GqVGk0bdoCffu+A1dXV72yN2/GICpqCS5duoDExAS4u3ugWrXq6NKlB8LDG+qVvXjxPH74YS0uXDiHpKREeHh4on79UPTq1RfVqgXkW/+RI4fg7Nm/0aJFK0yePE1vXlZWFt58sw2Sk5MwfPhovP12P4NiUpDZs2fg6NHD+PnnrQCy4x0ZORTLl69BjRo1TV5/ScHEi4iIiIggkwnw8HCBXF48iZdGIyIhIbXIyZeDgwPmzl0MAIiPj8Xq1SswevQwREWtg79/VbPU7cyZ0/jxx3UGJ14A0LRpC/Tq1RcajRr//HMFy5cvwbVrV7F69fcA5FK5P//ch88//xSBgTUxZswn8PLyxpUrl7Fu3SocPnwQCxYshYeHBwDg7t07GDLkHVStWhUjR34AT08vPHhwH3/9dQRnzpzWS7x+/XUTZs2ajuDgEIwaNQZ+fqXw+PEj7Nu3B+++2xeHDp0ssP5OTs44cuQQ0tLS4OzsLE3/668jUKvVBsfBGAEBNbB48UpUrFjZotuxNiZeRERERASZTIBcLuCHH7IQF1e05MdUfn4C3n7bDjKZUOTESyaToXbtoGfvghAYWBs9enTEr7/+gg8/HGf+yhrIy8tLqldwcAgyM1VYsmQBrly5jMDA7OmPHj3C1KlfoGbN2pg3bzEUiuxT85CQV/Haa2F4771+mDNnhtTrtH37bwCAOXMWwdHRUdpWhw5vQqvVSu+jo29gzpyv0bJlG/zf/30BQfgvmW7RojUOHz5QaP2DgoJx7doVHDr0J9q0aS9N37NnF954owl+/32ncYExgIuLa46/6YuDiRcRERERSeLiRNy7Z93Ey5xKly4Nd3cPPHhwHwCg1Wqxbt0qbN26BY8exaNUqdLo2rUHevZ8W1omLi4W8+fPwdmzfyM1NQXe3j5o3LgJIiPH6l3O2KhRfQBA3br1sGDB0iLVq2rVagCAhw8fSonXtm1bkJaWiiFDhktJV87yrVu3w65d2zF8+EOUKlUaKSkpcHFx0Uu6dGSy/4Zu2LhxPQRBQGTkWL2kS6dRoyaF1lehkKNp05bYu/d3KfFKS0vF0aOHMGXK13kmXhcvnsfSpYtw+fJFyOVyhIc3wujRY+Hp6SWVefQoHt98MxWnTp2Am5sSPXv2zrWevC41XL9+Hfbt2407d27Bzs4eNWvWwsiRY1ChQkVpua+++hxXr17GmDGfYP782bhz5zYqV66CsWMnoEaNQKnctm2/4scfv8f9+/fg6OiIihUrITLyQwQG1io0LqZg4kVEREREL4zU1BQkJyfBx8cXALBw4Vxs3Lge/foNRHBwCE6ePI5582YjLS0N77zzHgBgypTP8OhRPD744CN4enohNvYh/vnnCgCgY8fOiI+Pw549u6RLGl1cXIpcr9jYhwCAcuXKS9POnDkNNzcl6tatl+cyjRs3wY4dW3Hu3Bm0bt0OAQE1sHnzRnzzzVR07twN/v7V9BKunOutUaOmdImisVq1aovIyPeRkJAADw8PHDiwH05OzggNbZCr7MWL5zFq1PsIC2uIyZOnISMjHcuWfYdx4z7E0qWrpHLjx49FfHwsPvpoAlxdXbF27SrEx8dBLpfnWmdO8fGx6NatJ0qVKo20tFRs2fILhg17F+vXb4JS6S6Ve/LkMebOnYk+fd6BUumKhQvnYeLEj/DTT79CoVDg7Nm/MX36l+jdux/CwxsiIyMDV65cQkpKskmxMgQTLyIiIiKyabp7juLj47BgwRxoNBo0bdoCCQkJ+OWXDejVqw8GDx4GAHjttTCkpqbg++9Xo2fPt+Hs7IwrVy7h/fdHoEWL1tI627WLAAD4+ZWCr6/fc5c0Fk4Us+ul0Whw9eoVrF27Eg0bNkZgYE2o1VqpvqVLl853HaVLvwIgu0dOV6e//z6FX3/dhF9/3QRnZxe8+mp9dOjwpl4vVnx8vFkGpahTJxi+vn7Yv38vunTpjj17dqFZs5a5eucAYPHiBahRIxBTp34j9bJVruyPAQN64a+/DiM8vBGOHTuKq1cvY+7c7/Dqq6EAgODgeujWrQPc3T0KrEtk5FjptUajQWhoA0REtMb+/fvQqVNXaV5SUhLmz1+KKlX8oVDIIJfbYcyYEbh06SKCg+vi8uVLUCrdMWLEaGmZ119vZEqYDMbEi4iIiIhsVnp6Opo2DZPeu7kpMWbMJ2jQIBxHjx6GWq1G8+at9ZZp2bINfvttM65f/wfBwSGoXr0G1q9fB7lcgdDQBnq9UgXJOciEIAh6vTabN2/E5s0bpffly1fApElfGrWPukRGLpdj0qQv0bfvOzhy5BDOnz+DEyeO4dChA3jnnffw3ntDcy1jCkEQ0LJlG+zZswtNmzbH6dMnMXDg4FzlMjIycOHCOYwYMRoajUaaXqFCRXh7++DKlcsID2+Ey5cvwtXVVUq6AECpVKJevfqIjr5RYF0uXryA5cu/w7Vr/yApKVGafufObb1yPj6+qFLFX3pfuXIVANk9ZkD2wB1JSYn46qvP0bp1WwQF1c3z0k1LYOJFRERERDbLwcEBCxcuAyDAw8MDfn6lpMvvkpOTAADe3t56y3h7+wCAdAI/efI0LF26EEuXLsKsWdNRoUJFvP/+CDRp0jzf7T54cB89erwpvS9d+hVpOHQAaN68Fd5+ux8yM1U4duwI1q1bha+/noqvvpoulfH19cO1a//kuw3dkPS+vn5606tU8X+WXLyDp0+fYuzYkVi7diV69uwNpdIdvr6+0qWNpmrVqi2+/341fvhhLfz8SqF27Tq5yiQnJ0Gj0WDevNmYN292rvm6HrvHjx/Bw8Mz13wvL+8CE6+HDx/iww9HokaNQHz88QT4+PjCzs4OH3/8AVSqTL2yzw+/r1DYAQBUKhUA4NVXQ/F///cFNm78ER9+OAr29vZo2rQFRo8eq3fJoiUw8SIiIiIimyWTyfK9rE6pVAIAnjx5ope8PH786Nn87BNtHx8fTJz4GbRaLf755wpWr47CpEkT8MMPv6Bs2XJ5rtvHxxfLl6+R3tvZ2evN9/DwkOoVHFwX6enp+PnnH9G7dx/UqJE9iENIyKs4ffokzp07i+Dgurm2cfjwQchksgKfSebp6Yn27Tvi229n4s6dO6hVyx316tXHrl3bped8mcLfvyoqV66CDRu+R58+A/LsSXN1dYMgCOjXbyDeeKNprvm6Onh7+yAh4Wmu+U+ePC6wDsePH0V6ehq++uobuLm5AcjubczZ81UUbdq0R5s27ZGQkIDDh//EvHlzoFAoMGHCJKPWZ6jcd+OVQIcOHULfvn0RFhaG2rVro0WLFpg2bRqSkwu+Ca5fv34ICAjI9S86OtpKNSciIiKi4hIYWBsKhQJ//LFHb/off+yBk5MTqlevoTddJpMhMLAWBg8eDo1Gg3v37gIA7OzspB4THTs7O9SoUVP6V9gzw959dzCcnV2wevUKaVpERGc4O7tg2bJFuZ6NFRNzA7//vgPNmrVAqVLZ94Hll6DoLrfT9ex1794LWq0W8+bNhijmHqHy6NHDBdb1eX36DEDDho3Rtm2HPOc7OTmhdu0g3Lp1Uy8mun+vvJL90OjAwFpISUnB6dP/PUMsKSkJf/99qsDtZ2ZmQhAEvXvL/vhjr95ljcbw8PBARERnhIa+hlu3/jVpXYawiR6vxMREhISEYMCAAVAqlbh+/Trmz5+P69evY8WKFQUuW69ePYwbp/8Mh3Ll8v7lgoiIiIheHB4eHujevRd+/HEd7O3tERQUjFOnTuDXXzdh0KD34eTkhJSUFHz44Ui0adMeFSpUhFqtxs8/b4Crq5uUmFWsWBkajQY//bQeQUF14OLiggoVKhWpLkqlO7p164l161bh339volKlys962ibh888/xahR76N7917w8vLCP/9kD8ZRrlx5jBnz33nsqlXLcf36NbRs2QaVK1eBSqXCyZPHsXnzz2jcuKk0GIe/f1V8+OE4zJo1HY8exSMiohP8/Erh0aNH+PPPfThw4A8cPHjC4LrreogKMnz4aIwePQyTJk1Aixat4ebmhvj4OJw8eRzt23dEvXr1ERb2OqpXr4Evvvgfhg4dBTc3N6xZsxKurm4Frlt3T9jUqZPRqVNX/PtvDNavX1focnmJilqCxMQEhIS8Ck9PL0RH38Dx43/hrbf6FHldRWUTiVdERAQiIiKk9w0aNIC9vT3+7//+D7GxsShVqlS+yyqVStStW9cKtSQiIiKyfX5+pg/KUJK2OXx4JNzc3LB16xasXbsSpUqVxsiRH0gn2vb29vD3r4pfftmA2NiHcHBwRI0agZgzZ4E0HHvDho3RpUsPrFu3Ck+fPkFwcEiRn+MFAL1798Uvv/yE779fjU8//RwA0LRpCyxe/ArWrFmJ2bOnIy0tDX5+pRAR0Rn9+g3Uu2epdev2UKlU+OWXDYiPj4dcLkPp0mUwfHgkunTpobetTp26okoVf/zww1rMnTsLyclJ8PDwRN269bBoUZRxwSxAUFAwFi1ajqioJZg2bTKysrLg61sK9euHSoOVCIKA6dNnYebMafjmm2lwc3ND9+69EB8fW2AvnL9/VUyYMAkrVy7DJ5+MQbVq1TFlygz83/+NL3I9a9SoiZ9+Wo8//tiLtLRU+Pr6oXfvfhgwYJDR+24oQcyr/9EG7N69G6NGjcIff/yBsmXL5lmmX79+cHZ2xpIlS8y6bY1GiydPUo1eXqGQwdPTBU+fpkrDiZLlMN7moYtj+rfzob13P88yMpkAJyd7pKeroNXa5KHF7GRly8Dpg1EWaX9s29ZjjVjrtvHttyqbfnitOYSEyPD223aYPz8Lt2+/vG27bFkBH3xgb/Z2l5WlwuPHD+Dt/Qrs7OyhUMigVmshkwnw8HCBXG79xAsANBoRCQmpL/z3hy7eZHnmiPXzn5fneXm5QC437O4tm+jx0tFoNFCr1bhx4wYWLlyIZs2a5Zt06Zw4cQJ169aFRqNBcHAwRo8ejdDQ0AKXISIiInrZaLXZiY9MVjyJl1YrvvBJF73cbCrxatasGWJjs4ejbNy4MWbPzj1cZU6hoaHo1KkTKlWqhLi4OERFRWHgwIFYu3YtQkLyHx3GEAqF8eOS6LJiQ7NjMg3jbR66+AmCkO+Xsm56cX1pl0T/PXvF/O2Pbdt6rBFr/c+YxTZjE3T7X9Dx5mVgqeOHVvtfTHUD1AlC9gN/mfxY1vPxJssxd6zlcsGk83/AxhKvpUuXIi0tDTdu3MCiRYswdOhQrFy5Uu9hdTlFRkbqvW/atCkiIiKwaNEiLFu2zOh6yGQCPD1djF5eR6l0MnkdZDjG2zwcHe0Ap9xd7Tk5ONhZqTY2wDE7FpZsf2zb1mONWDs62sHpJf+T2tnp/le81LHQPdPV3O0uI0OOR49kkMsF/oBTTBhv6zE11lqtAJlMBnd3Z5MftGxTiVeNGtkjy9SrVw81a9ZEt27dsGfPHrRt29ag5Z2dndGkSRP8/vvvJtVDqxWRlJRm9PJyuQxKpROSktKh0fAaX0tjvM1DF8eMjCyI6ao8y8hkAhwc7JCZmcVfTJ8RMrLgCFik/bFtW481Yp3zM5ae/nJ/frKyBAB2yMpSIz395W3bGRnZcTB3u1OpMqHVaqHRiNBotJDLZdBotOyBsQJBAONtJeaKtUYjQqvVIjExDenpuYevVyqdXsx7vHIKDAyEXC7H7du3i7ScucYSMcdNkRqNljdXWhHjbR6iWPhlKLxU5T+yZ8ccS7Y/tm3rsUasDfmMveh0l8K97LHQnbKYu91pNP/FVLcNJgHWwXhbj7ljrdGIJn8Obbaf88yZM9BoNEV6JldaWhoOHDiAoKAgC9aMiIiIqOSz0YGtiazKnJ8Tm+jxGjlyJGrXro2AgAA4Ojri6tWrWL58OQICAtCyZUsAwMSJE7FlyxZcvnwZAHDq1ClERUWhVatWKFOmDOLi4rBy5UrEx8dj7ty5xbk7RERERMVGd2+8SpUJe3uHYq4NUcmmUmUCAORy09Mmm0i86tSpgx07dmDp0qUQRRFly5ZFz549MWjQINjbZ9/kn32t8n/XXfr6+kKlUmH27NlISEiAk5MTQkJCMHnyZNSpU6e4doWIiIioWMlkcjg5uSIl5SkAwNnZEZrct66QhWi1gt7lnmQ5psRaFEWoVJlISXkKJydXyMww3KxNJF5DhgzBkCFDCiwzffp0TJ8+XXpfsWJFREWZ/6ncRERERLZOqfQCAKSkPEVamgxaLe8TtRaZjPG2FnPE2snJVfq8mMomEi8iIiIiMh9BEODu7g1PTy+4uNgjMTGNvTBWIJcLcHd3ZrytwByxlssVZunp0mHiRURERPSSksnkcHR0RHq6hqOjWoFCIWO8raQkxtpmRzUkIiIiIiKyFUy8iIiIiIiILIyJFxERERERkYUx8SIiIiIiIrIwJl5EREREREQWxsSLiIiIiIjIwph4ERERERERWRgTLyIiIiIiIgtj4kVERERERGRhTLyIiIiIiIgsjIkXERERERGRhTHxIiIiIiIisjAmXkRERERERBbGxIuIiIiIiMjCmHgRERERERFZGBMvIiIiIiIiC2PiRUREREREZGFMvIiIiIiIiCyMiRcREREREZGFMfEiIiIiIiKyMCZeREREREREFmYTidehQ4fQt29fhIWFoXbt2mjRogWmTZuG5OTkQpfdvHkz2rZti6CgIERERGDnzp1WqDEREREREdF/FMVdAUMkJiYiJCQEAwYMgFKpxPXr1zF//nxcv34dK1asyHe5Xbt2Yfz48RgyZAgaNmyIvXv3YsyYMXBzc0OjRo2suAdERERERPQys4nEKyIiAhEREdL7Bg0awN7eHv/3f/+H2NhYlCpVKs/l5s6di7Zt22Ls2LEAgLCwMNy8eRPz5s1j4kVERERERFZjE5ca5sXDwwMAoFar85x/584dxMTE6CVsQHYSd/78eTx58sTSVSQiIiIiIgJgY4mXRqNBZmYmLl26hIULF6JZs2YoW7ZsnmVjYmIAAFWqVNGb7u/vD1EUpflERERERESWZhOXGuo0a9YMsbGxAIDGjRtj9uzZ+ZZNTEwEACiVSr3p7u7uevONpVAYn7PK5TK9/8myGG/z0MVPEATIZEKeZXTT85v/MhKE7FhYov29yG27oHZWHHR1sbOTWyzeum3I5QIUCuP2XRQBoeSEzWhy+X/HElO+bw0lioAoihbfTlHpjh/WaHfGbEMQ/qujpZXUv1FR/fc5f/GO2yVNSfyOtKnEa+nSpUhLS8ONGzewaNEiDB06FCtXroRcLs93mecPCLoPrSkHCplMgKeni9HL6yiVTiavgwzHeJuHo6Md4GRfYBkHBzsr1cYGOGbHwpLt74Vs21otICs5X5Y6rq6OFt+Gvb0dHByMXPgFybwUCt3/CuNjURQlNG4+PtkfBWu0O2tswxQl9JBgHK32xTxul1AlKdY2lXjVqFEDAFCvXj3UrFkT3bp1w549e9C2bdtcZXP2bPn4+EjTk5KSAOTuCSsKrVZEUlKa0cvL5TIolU5ISkqHRqM1ej1kGMbbPHRxzMjIgpiuyrOMTCbAwcEOmZlZ0Gpt/5dJcxAysuAIWKT9vahtW7dfqvUboI2LL+7qAMhu2/b2CqhUaou1bcHXFw5vvwX1hYtQ3yj6d4zg4w15VX9oLl6CmJJqgRpaj8bRC0BVaG7cgPqCZe/JFlycIQ+qXSKPW9k9v3bYsEGNhw8tUzeZLDvZV6myoC3CYUS3nOb6DYgZGRapm45feSf0GOePzB82QIwvGccEY8lL+8HurZ5ISclAVpamuKvzQrPWd6RS6WRwr5pNJV45BQYGQi6X4/bt23nO193bFRMTA39/f2l6dHQ0BEHIde9XUanVpv8BNRqtWdZDhmG8zUMUxUJPTrTawsu8LGTPetkt2f5e1LatiY2D9t794q4GAECUCYCTPTTpKou1bV1bEVNSoU0q/DmVuZZ3ds6xfJJZ62ZtYsazfUnPsPi+CBAhR8k8bolidi/cw4ci7t61zGdcJhPg5ASkpxdt/3XLZV18DNGI9loUYoobAH9o40rOMcEUdshuby/icbskKknfkTbbaXvmzBloNBqUK1cuz/nly5dHlSpVsGPHDr3p27ZtQ506deDl5WWNahIREREREdlGj9fIkSNRu3ZtBAQEwNHREVevXsXy5csREBCAli1bAgAmTpyILVu24PLly9JykZGRGDNmDCpUqIDXX38d+/btw5EjR7B8+fLi2hUiIiIiInoJ2UTiVadOHezYsQNLly6FKIooW7YsevbsiUGDBsHePvsmf61WC41G/1rZdu3aISMjA4sXL0ZUVBQqVqyIOXPm8OHJRERERERkVSYnXv3790evXr3QqlUr2NlZZiSzIUOGYMiQIQWWmT59OqZPn55repcuXdClSxeL1IuIiIiIiMgQJideJ06cwMmTJ+Hh4YEuXbqgZ8+eqFSpkhmqRkRERERE9GIweXCNjh07wt7eHk+fPsXKlSvRrl07DBgwADt27EBWVpY56khERERERGTTTE68vvnmGxw6dAiffvopqlWrBlEUcfz4cYwdOxZvvPEGvvnmG/z7779mqCoREREREZFtMstw8kqlEv369cNvv/2GDRs2oGvXrnB0dMTTp0+xYsUK9oIREREREdFLzeyjGgYHByM4OBgTJ07E1q1bsXHjRly+fBnHjx/HiRMn4OHhga5du6JHjx68F4yIiIiIiF4KFnuAsqurK3r37o1NmzZh06ZNqF+/PkRRREJCgtQL9u677+Lw4cOWqgIREREREVGJYNHneGVkZGD79u346aefcP78eQCAKIpwd3dHYmIijh49ir/++gvNmjXDrFmz4OTkZMnqEBERERERFQuLJF5XrlzBhg0bsG3bNqSmpkIURSgUCjRv3hy9e/dGeHg4Ll26hLVr12Lr1q3Yv38/5s2bh3HjxlmiOkRERERERMXKbIlXWloatm3bhg0bNuDy5csAsnu3ypQpgx49eqBHjx7w8fGRyteqVQvTp09Hu3bt8P7772PXrl1MvIiIiIiI6IVkcuJ14cIF/PTTT9i+fTvS09MhiiJkMhkaNWqE3r17o2nTphAEId/lmzRpAi8vL8TGxppaFSIiIiIiohLJ5MSrR48eEAQBoijC29sb3bt3R8+ePVG2bFmD1+Ho6AhRFE2tChERERERUYlklksN69evj969e6N169ZQKIq+yvXr10OtVpujKkRERERERCWOyYnX9u3b4e/vb9I6SpUqZWo1iIiIiIiISiyTn+NlatJFRERERET0ojO5xyslJQXHjx+Hi4sLwsLCCiz7119/IS0tDWFhYXBxcTF100RERERERDbB5B6vbdu2YeTIkTh48GChZXft2oWRI0dix44dpm6WiIiIiIjIZpiceO3ZswcA0KFDh0LLdu3aFaIoYvfu3aZuloiIiIiIyGaYnHjdvHkTAFC1atVCywYEBOgtQ0RERERE9DIwOfF69OgRlEolHBwcCi3r6OgId3d3PHr0yNTNEhERERER2QyTEy8nJyekpqZCo9EUWlatViM1NRV2dnambpaIiIiIiMhmmJx4VaxYERqNBn/99VehZf/66y+o1WqUL1/e1M0SERERERHZDJMTrzfeeAOiKOLrr79GSkpKvuVSU1Px9ddfQxAENGnSxNTNEhERERER2QyTE6++ffvC3d0d169fR/fu3bFnzx5kZGRI8zMyMrB7925069YN169fh5ubG/r372/qZomIiIiIiGyGyQ9Q9vDwwKxZszBixAj8+++/iIyMhFwuh4eHBwRBwNOnT6HRaCCKIhwcHPDtt9/C09PTHHUnIiIiIiKyCSb3eAFAo0aNsH79etSrVw+iKEKtVuPRo0eIj4+HWq2GKIoIDQ3Fhg0b8Prrrxd5/Tt37sTw4cPRpEkT1K1bFx07dsQPP/wArVZb4HL9+vVDQEBArn/R0dHG7ioREREREVGRmdzjpVOzZk388MMPuHXrFs6cOYP4+HgAgK+vL+rVq4cKFSoYve6VK1eiTJky+OSTT+Dt7Y3jx4/jq6++wp07dzBu3LgCl61Xr16uMuXKlTO6LkREREREREVltsRLp2LFiqhYsaJZ17l48WJ4eXlJ78PCwpCWlobvv/8eY8aMgb29fb7LKpVK1K1b16z1ISIiIiIiKgqzXGpoaTmTLp3AwEBkZmYiISHB+hUiIiIiIiIqAptIvPJy+vRpeHh4wNvbu8ByJ06cQN26dREUFIS+ffvi5MmTVqohERERERFRNrNcaiiKIjZv3ozt27fjn3/+QUJCAjQaTb7lBUHA5cuXjd7ehQsXsGnTJowYMQJyuTzfcqGhoejUqRMqVaqEuLg4REVFYeDAgVi7di1CQkKM3j4AKBTG56xyuUzvf7Isxts8dPETBAEymZBnGd30/Oa/jAQhOxaWaH8vats2pK1ZmzXatq6tCIIgvS7aCnTrgXHLl0ACLL8vAkrucUu36zKZ5epnbNvWlRcg/FdRC8n52SiJf6eiyBlvU84lqXAl8TvS5MRLpVJhyJAhOH78OERRNEedChQfH4/IyEgEBQVh8ODBBZaNjIzUe9+0aVNERERg0aJFWLZsmdF1kMkEeHq6GL28jlLpZPI6yHCMt3k4OtoBTvnfVwkADg52VqqNDXDMjoUl29+L2rYNaWvWZtG2/aytKBQy2Nnl/6NivnKcZMiNWb4EUeTYF6NiUaSNZW+rJB63HByy/7e3t4OThT/mxu6/QiEDLPw30iUoJfGYUGT22aferq6OxVyRl0dJ+o40OfFaunQpjh07BgBo3bo1mjdvjlKlShXYE2Ws5ORkDB48GI6Ojvjuu+9gZ1e0g4SzszOaNGmC33//3aR6aLUikpLSjF5eLpdBqXRCUlI6NJqCh8Qn0zHe5qGLY0ZGFsR0VZ5lZDIBDg52yMzMglZr+R9ibIGQkQVHwCLt70Vt24a0NWuzRtvWtRW1WousrPyvGsl3eY0WCgAajRZaI5YvSdTP2rNGY1wsikJQZ8etJB63MjMFAHZQqbKQnm6ZuhnbtnXLqdVaiBb+G6nV2e2hJB0TjCVXqWEPICUlw+Jt+2Vnre9IpdLJ4F41kxOvHTt2QBAEjBgxAiNHjjR1dfnKzMzEsGHD8OjRI2zYsMHohzCbq1dOdxAwhUajNct6yDCMt3mIoljol7NWW3iZl4Xs2THHku3vRW3bhrQ1a7Nk29a1FVEUjfquEp4tIorm+64rbiKssS/Z6y+Jxy1RzL4sTauFxetm7P6LMK69FmkbOT4bJe1vVFSC9r/29iIet0uikvQdaXLidffuXQiCgIEDB5qjPnlSq9UYPXo0rl69inXr1qFs2bJGrSctLQ0HDhxAUFCQmWtIRERERESUP5MTLxcXF2i1Wri4mH7PU36++OIL7N+/Hx9//DEyMjJw9uxZaV7VqlXh6uqKiRMnYsuWLdKgHadOnUJUVBRatWqFMmXKIC4uDitXrkR8fDzmzp1rsboSERERERE9z+TEKygoCIcPH0ZCQgI8PDzMUKXcDh8+DAD45ptvcs1bs2YNGjRoAK1WqzeSoq+vL1QqFWbPno2EhAQ4OTkhJCQEkydPRp06dSxSTyIiIiIioryYnHgNHDgQhw4dwqpVq/DBBx+YoUq5/fHHH4WWmT59OqZPny69r1ixIqKioixSHyIiIiIioqIweWD78PBwfPTRR1i2bBkWLlyI9PR0c9SLiIiIiIjohWFyj1f//v0BZN/rtWDBAixduhRVq1Yt8J4vQRCwevVqUzdNRERERERkE0xOvE6cOKH3PjMzE5cuXSpwGUs/hZ6IiIiIiKgkMTnxsuSzu4iIiIiIiF4ETLyIiIiIiIgszOTBNYiIiIiIiKhgTLyIiIiIiIgszORLDXXS09OxceNGHD58GPfv30dGRgb27t0rzU9OTsaff/4JQRAQERFhrs0SERERERGVeGZJvK5cuYLhw4fj4cOHEEURQO6RC11dXbF48WLExMSgfPnyCA4ONsemiYiIiIiISjyTLzV8+vQphgwZggcPHqBmzZoYN24cXF1dc5UTBAHdu3eHKIp6PWFEREREREQvOpMTr9WrVyM+Ph7h4eHYuHEjBg4cCEdHxzzLNmnSBABw/PhxUzdLRERERERkM0xOvPbv3w9BEPDxxx9DJit4dVWqVIFCocCtW7dM3SwREREREZHNMDnxun37Nuzs7BAYGGhQeVdXV6Smppq6WSIiIiIiIpthcuIliiLkcnmuwTTyotVqkZqaCicnJ1M3S0REREREZDNMTrx8fX2RkZGBx48fF1r27NmzyMrKQtmyZU3dLBERERERkc0wOfGqX78+AGDz5s0FltNqtZg7dy4EQUB4eLipmyUiIiIiIrIZJide/fv3BwAsXrw439EKHzx4gOHDh+P48eOQy+V4++23Td0sERERERGRzTD5AcqBgYEYNmwYFi1ahIEDB6J27dpISUkBAEyePBnXrl3DuXPnoNFoAAAffvghypcvb+pmiYiIiIiIbIbJiRcAREZGQqlUYu7cuTh//rw0/ccff4QoigAAR0dHjB07Fv369TPHJomIiIiIiGyGWRIvAHjnnXfQqVMnbN26FadPn0ZcXBy0Wi18fHxQr149dOrUCT4+PubaHBERERERkc0wW+IFAJ6enujfv7903xcRERERERGZYXANIiIiIiIiKhgTLyIiIiIiIgsz+VLDBQsWGLXcyJEjTd00ERERERGRTTBL4iUIgsHlRVGEIAhFSrx27tyJrVu34tKlS0hMTET58uXRu3dv9OrVCzJZwZ12mzdvxpIlS3Dv3j1UrFgRI0aMQLt27QzeNhERERERkalMTrxCQ0MLnJ+SkoLo6GioVCq4u7ujevXqRd7GypUrUaZMGXzyySfw9vbG8ePH8dVXX+HOnTsYN25cvsvt2rUL48ePx5AhQ9CwYUPs3bsXY8aMgZubGxo1alTkehARERERERnD5MRr7dq1hZZJTU1FVFQUFi9ejObNm2PgwIFF2sbixYvh5eUlvQ8LC0NaWhq+//57jBkzBvb29nkuN3fuXLRt2xZjx46Vlrt58ybmzZvHxIuIiIiIiKzGKoNruLi4IDIyEu+99x6++eYbHD9+vEjL50y6dAIDA5GZmYmEhIQ8l7lz5w5iYmIQERGhNz0iIgLnz5/HkydPilQHIiIiIiIiY1l1VMOBAwdCFEVERUWZvK7Tp0/Dw8MD3t7eec6PiYkBAFSpUkVvur+/P0RRlOYTERERERFZmlkfoFwYT09PKJVKnD9/3qT1XLhwAZs2bcKIESMgl8vzLJOYmAgAUCqVetPd3d315htLoTA+Z5XLZXr/k2Ux3uahi58gCJDJ8h5QRzc9v/kvI93gQ3Z2crO3QV2cLbFuaxMEAbpxmqR25OdXpMGbLEkmEwB7BeQqNQStaJFtCL6+2f8LgnH7/WwRQUCJiZupBFh+X4RngZPLZZDJLPO3NZbuFEcuF0w67yiILr5F3X/dcgIEwNJ/I922Cvj+sRU5vyeN/Zu+CHEwB61WhCjm32ZL4vmfVROvlJQUJCUl5XtPliHi4+MRGRmJoKAgDB48uNDyzx+wdX8gUw7kMpkAT08Xo5fXUSqdTF4HGY7xNg9HRzvAqeDPsIODnZVqYwN8PAGtFq6ujhbbhCXXbS1aLfD8ILUOb79VPJUpgPHfXoZTODvCzi6t6AvmOMmQ2+X9o6StUOTYFztL74uzIyCKsLe36imRQezsdP8r4OBg2W0Zu/8KhQyw8N9Il6AY8v1T4j2LsynH7byOly8jQ+NQks7/rHqUWblyJURRRLly5YxaPjk5GYMHD4ajoyO+++472Nnlf3KXs2fLx8dHmp6UlAQgd09YUWi1IpKSjPhSfEYul0GpdEJSUjo0Gq3R6yHDMN7moYtjRkYWxHRVnmVkMgEODnbIzMyC1kK9ArZGJijgIJMha8NP0DyMM++6ZQLs7RVQqdQ2HW/B1xcOb7+FjTOiEXcnHYKHO+Tly0Nz4SLEVOOPtWYlZCcDao0WsFCoq4V5ofX7VaEWZMjK0hR5eUGjhQKARqOF1ojlSxL1s2O1RqM1KhZFIQgyKAQBmouXIKakWnRbRaV29AJQFerrN6C+aJl70wUh+/iu0WhRQOdB7uV8vCGv6m+V9qZWZ7eHgr5/bIVcpYY9gJSUDKPatu67eP16NeLibPe4byo/PwG9eysKPLez1vmfUulkcK+ayYnXyZMnC5yfmZmJBw8eYPfu3Th8+DAEQUCHDh2KvJ3MzEwMGzYMjx49woYNG+Dp6Vlged29XTExMfD395emR0dHQxCEXPd+FZXuIGAKjUZrlvWQYRhv8xBFsdCTfK228DIvC+HZmYzmYRw0d++Zdd2iTACc7KFJV9l0vGXPYhR75RHuX0uGrDSgUJSH+kYatM9+LCtugiDAzk6OrCxNgZe2mMKngu5X2YIvn8mP8GwRUYTF6mhtIiy/L1LcUlJLTHuTpD9rExnpFqubIAiQ28mhLWLbljk7A7BOe9Ot35Dvn5JOd6myViuadE4SG6vFvXu2HQtTiGL2lWuGnNuVpPM/kxOvfv36GXTZnu5D06BBA7z33ntF2oZarcbo0aNx9epVrFu3DmXLli10mfLly6NKlSrYsWMHWrVqJU3ftm0b6tSpk+dIiURERERERJZglksNC76xTQ6lUokaNWqgQ4cO6Nq1K2RFvDD1iy++wP79+/Hxxx8jIyMDZ8+eleZVrVoVrq6umDhxIrZs2YLLly9L8yIjIzFmzBhUqFABr7/+Ovbt24cjR45g+fLlRd5HIiIiIiIiY5mceF29etUc9SjQ4cOHAQDffPNNrnlr1qxBgwYNoNVqodHoXyvbrl07ZGRkYPHixYiKikLFihUxZ84cPjyZiIiIiIisquQN4ZOHP/74o9Ay06dPx/Tp03NN79KlC7p06WKJahERERERERmEg1ESERERERFZGBMvIiIiIiIiCzP5UsMJEyaYox4QBAFTp041y7qIiIiIiIhKEpMTr82bNxs0nLzO8yMgCoIAURSZeBERERER0QvL5MSrc+fOEAQB+/btQ1JSEhwcHFCrVi2ULl0aABAbG4tLly4hIyMD7u7uaN68ucmVJiIiIiIisiUmJ17Tp0/H2LFjkZycjPfffx+DBw+Gq6urXpmUlBQsW7YMS5cuhUqlwqxZs0zdLBERERERkc0wOfH66aefsGPHDowcORIjRozIs4yrqyvGjBkDe3t7LFiwAGFhYejRo4epmyYiIiIiIrIJJo9q+PPPP0Mmk2HAgAGFlh0wYABkMhk2btxo6maJiIiIiIhshsmJV0xMDFxdXXNdXpgXXbmYmBhTN0tERERERGQzTE68tFotkpOTkZCQUGjZhIQEJCcnQ6vVmrpZIiIiIiIim2Fy4hUQEABRFLFw4cJCyy5atAharRbVq1c3dbNEREREREQ2w+TEq3fv3hBFEevWrcOECRNw586dXGXu3LmDCRMmYO3atRAEAW+//bapmyUiIiIiIrIZJo9q+Oabb+Kvv/7C5s2bsWXLFmzZsgWvvPIK/Pz8IAgCYmNj8eDBAwDZD0/u3Lkz3nzzTZMrTkREREREZCtMTrwAYNq0aQgMDMTChQuRmJiI+/fv4/79+3pl3N3dMWzYMINGPyQiIiIiInqRmCXxAoD+/fujV69eOHz4MC5evIjHjx8DALy9vVG7dm00bNgQDg4O5tocERERERGRzTBb4gUA9vb2aN68OZo3b27O1RIREREREdk0kwfXICIiIiIiooKZtcfr6tWrOHz4MO7fv4/09HRMmzZNmpeVlYUnT55AEAT4+fmZc7NEREREREQlmlkSr+TkZEycOBF79+4FkD16oSAIeomXWq1Gp06dkJSUhN9//x3ly5c3x6aJiIiIiIhKPJMvNVSr1Rg8eDD27t0LR0dHNGnSJM9BNJycnNCtWzdotVopQSMiIiIiInoZmJx4/fzzzzh79izKly+PXbt2YfHixXBzc8uzbJs2bQAABw8eNHWzRERERERENsPkxGvbtm0QBAETJkxAqVKlCiwbGBgImUyGGzdumLpZIiIiIiIim2Fy4nXt2jUIgoCGDRsWWtbOzg5ubm5ISEgwdbNEREREREQ2w+TBNdLS0uDi4gJ7e3uDyqtUKigURdvsrVu3EBUVhXPnzuH69euoUqUKtm3bVuhy/fr1w4kTJ3JN37FjB/z9/YtUByIiIiIiImOZnHh5enri0aNHSEtLg7Ozc4Fl//33X6Snp6NChQpF2sb169dx4MABBAcHQ6vVQhRFg5etV68exo0bpzetXLlyRdo+ERERERGRKUy+1DAoKAgA8OeffxZadtWqVQCyk6GiaN68OQ4cOIB58+ahVq1aRVpWqVSibt26ev/yGnWRiIiIiIjIUkxOvLp27QpRFDFv3jzEx8fnW2758uX48ccfIQgCunXrVrRKykyuJhERERERUbEx+VLDli1bomnTpvjzzz/RrVs3vPnmm8jMzAQA7Ny5E9euXcOuXbvw77//AgDat2+P0NBQUzdrsBMnTqBu3brQaDQIDg7G6NGjrbp9IiIiIiIikxMvAJg9ezY+/vhj7Nu3D1FRUdL0Dz/8EACke7JatWqFqVOnmmOTBgkNDUWnTp1QqVIlxMXFISoqCgMHDsTatWsREhJi0roVCuN74eRymd7/ZFmMt3no4icIAmQyIc8yuun5zX8ZCcJ/MRHNHJcXJd66GAmCkP1a0E3/b15xE4Sc/1uoTtK+CsbtdwmMm6kEWGFfSnLcTG0TRdhEkdu2FeOW8xhh68e7nMdtY84l9b+LzVo1m6JrEwWd25XE8z+zJF7Ozs5YuHAh/vzzT2zcuBF///03nj59CgBwc3NDvXr10LNnT7Ro0cIcmzNYZGSk3vumTZsiIiICixYtwrJly4xer0wmwNPTxdTqQal0MnkdZDjG2zwcHe0Ap4JHMXVwsLNSbWyAQ/Zh1t5eUWjcjN6ErcfbMbv+CoUMdnZyIMeXpdxOXpw1y0WhsFx9FM/2WyF/FoeiKsFxKypFjn0xKhZFUYLjZnKbKMq2itq2rRg3XYJiyPdPiWef/Z3g6upo0mocHe3g9BKf1jg+C58h53Yl6fzPLImXTtOmTdG0aVMAgFqthlarNXiYeWtwdnZGkyZN8Pvvv5u0Hq1WRFJSmtHLy+UyKJVOSEpKh0ajNakuVDjG2zx0cczIyIKYrsqzjEwmwMHBDpmZWdBqDR999EUmy1TDAYBKpYYmn7gZve4XJN5CRhYcAajVWmRlaSBotFAA0Gi00GZpirt6ALJ/1Vco5FCrNSjCwLpFon52fFJrsuNQVCUxbsbSxUJjZCyKoiTHzdQ2YQhj27Y146ZWZ8ehoO8fWyFXqWEPICUlw6i/ac7v4vR02z3umyojQwBgV+C5nbXO/5RKJ4N71UxOvJo3bw6ZTIaoqChUrFjxvxUX8Vld1lKUoegLojsImEKj0ZplPWQYxts8RFEs9CRfqy28zMtCeHbMsWRMbD3esmcxEkURoihCeLYromi+Y7bpsi9rsWidpPWKRm2jZMbNNCIsvy8lOm4mtgnDGNe2rRk3MccxwpaPdQAgaP/7TjDlnORFiIUpdE3OkHO7knT+Z3J2FB8fDzs7O72kq6RKS0vDgQMHpCHwiYiIiIiIrMHkxMvPzw9PnjwxR13ylZ6ejgMHDgAA7t27h5SUFOzatQsA8Nprr8HLywsTJ07Eli1bcPnyZQDAqVOnEBUVhVatWqFMmTKIi4vDypUrER8fj7lz51q0vkRERERERDmZnHi9/vrr+Pnnn3H58mXUrFnTHHXK5fHjxxg9erTeNN37NWvWoEGDBtBqtdBo/rtW1tfXFyqVCrNnz0ZCQgKcnJwQEhKCyZMno06dOhapJxERERERUV5MTryGDBmC7du344svvsDKlSvhZIEhVsqVK4d//vmnwDLTp0/H9OnTpfcVK1bUG9qeiIiIiIiouJiceMnlcnzxxReYNGkSIiIi0K9fP4SEhMDLywtyef7Di5YpU8bUTRMREREREdkEkxOvnM/mSk9Px4wZMwpdRhAE6V4sIiIiIiKiF53JiZcxQ4iWuOFaiYiIiIiILMjkxGvfvn3mqAcREREREdELq8iJV40aNeDr64tDhw4BAMqWLSvNi46ORlZWFmrUqGG+GhIREREREdk4o3q88rtUcMCAAXjy5Anv3yIiIiIiIspBZu4V8v4tIiIiIiIifWZPvIiIiIiIiEgfEy8iIiIiIiILY+JFRERERERkYUy8iIiIiIiILIyJFxERERERkYUZNZz848ePERgYmO/8guYBgCAIHHKeiIiIiIheGmZ9jhcRERERERHlVuTEa+TIkZaoBxERERER0QuLiRcREREREZGFcXANIiIiIiIiC2PiRUREREREZGFMvIiIiIiIiCyMiRcREREREZGFMfEiIiIiIiKyMCZeREREREREFsbEi4iIiIiIyMKYeBEREREREVmYTSRet27dwqRJk9CpUyfUrFkTERERBi+7efNmtG3bFkFBQYiIiMDOnTstWFMiIiIiIqLcbCLxun79Og4cOICKFSvC39/f4OV27dqF8ePHo1WrVli2bBnCwsIwZswYHD582IK1JSIiIiIi0qco7goYonnz5mjZsiUAYPz48bh48aJBy82dOxdt27bF2LFjAQBhYWG4efMm5s2bh0aNGlmsvkRERERERDnZRI+XTFb0at65cwcxMTG5LkuMiIjA+fPn8eTJE3NVj4iIiIiIqEA2kXgZIyYmBgBQpUoVven+/v4QRVGaT0REREREZGk2camhMRITEwEASqVSb7q7u7vefGMpFMbnrHK5TO9/UwiCAJlMMHk9LzJdfOzs5FLMtVoRoigWZ7Vsji52BbU53fTC2qTg4Q44u5i3giWUzMsLACAv5Zt3AYUMUGuNW7dMAOwVkKvUELS2254F3+zYCIIAQRCAZ81HELKnlQSCkPN/C9VJ2lfBuP0ugXEzlQAr7EtJjpupbaIImyhy27Zi3HTrl/n5lby/URHpvguyzyOLfhqu+37N/i42Z81si64dFHQubc7zbXN5YRMvnec/oLqTbVM+uDKZAE9P008alUonk9cBrRYv9SevCFxdHf97w7gZzdHRDnCyL7CMg4Nd/jPd3aEdPhKygsq8gOx6vYW89lirFU3+8aTgv4btUDg7ws4uDcjxZSm3kxdzrfQpFJarj+LZfivkMtgZs98lOG5FpcixL0bFoihKcNxMbhNF2VZR27YV4+bh5witFnB4+y2LbseanJ0dTFre0dEOTmY4jbRVjs9O6Qw5lzbL+baZvLCJV86eLR8fH2l6UlISgNw9YUWh1YpISkozenm5XAal0glJSenQaIz7pTvnelTrN0AbF2/0el50MpkAe3sFVCp19kmuny/se79lcvxfNrr2lpGRBTFdlWcZmUyAg4MdMjOzoM2nB0bwtIOjgx1+mnwR8f8a/zmyGd5eUFSrCs2FixBT9fe3WpgXWr9fFRtnRCPu6uOir1vIPiFTa7SA7XZ4SXFQCzJkZWkgaLRQANBotNBmaYq7egCyf9VXKORQqzWwVGe5+tnxSK3RIsuI/S6JcTOWLhYaI2NRFCU5bqa2CUMY27atGTeFkwwyGYw/VpYggosz5EG1kZWlhkZT9INJQICAtm0VUKmykJ5uwwd+E2VkCADsCjyXM9f5dmGUSieDe9Ve2MRLd29XTEyM3hD00dHREAQh171fRaU28tKgnDQarXnWExsH7b37Jq/nRSXKBMDJHpp0ld4lhuaK/8tGFMV8kyodrTb/MrJn8Y+7mYr715LNXr+SRlbaGQpXQH0jDdpnP/zo+FTI/hUu7k4a7v2TlNfiBRIEAXZ2cmRlaWz60lldHIDsz6fwbFdEESVov7J7JS1aJ2m9xl0KXTLjZhoRlt+XEh03E9uEYYxr21aNm+57w8hjZUkiUwIKHyAzUzTqHMTHR3fLBAr9Ln6R6ZqcIedyJel874W91qp8+fKoUqUKduzYoTd927ZtqFOnDrye3XdBRERERERkaTbR45Weno4DBw4AAO7du4eUlBTs2rULAPDaa6/By8sLEydOxJYtW3D58mVpucjISIwZMwYVKlTA66+/jn379uHIkSNYvnx5sewHERERERG9nGwi8Xr8+DFGjx6tN033fs2aNWjQoAG0Wi00Gv1rjNu1a4eMjAwsXrwYUVFRqFixIubMmcOHJxMRERERkVXZROJVrlw5/PPPPwWWmT59OqZPn55repcuXdClSxdLVY2IiIiIiKhQL+w9XkRERERERCUFEy8iIiIiIiILY+JFRERERERkYUy8iIiIiIiILIyJFxERERERkYUx8SIiIiIiIrIwJl5EREREREQWxsSLiIiIiIjIwph4ERERERERWRgTLyIiIiIiIgtj4kVERERERGRhTLyIiIiIiIgsjIkXERERERGRhTHxIiIiIiIisjAmXkRERERERBbGxIuIiIiIiMjCmHgRERERERFZGBMvIiIiIiIiC2PiRUREREREZGFMvIiIiIiIiCyMiRcREREREZGFMfEiIiIiIiKyMCZeREREREREFsbEi4iIiIiIyMIUxV0BQ928eRNTpkzB6dOn4eTkhA4dOuCjjz6Co6Njgcv169cPJ06cyDV9x44d8Pf3t1R1iYiIiIiIJDaReCUlJWHAgAEoU6YM5s2bhydPnmDatGlISEjAzJkzC12+Xr16GDdunN60cuXKWaq6REREREREemwi8frxxx+RlJSELVu2wMvLCwAgl8vx0UcfYdiwYYX2XCmVStStW9cKNSUiIiIiIsrNJu7xOnjwIMLDw6WkCwDatGkDe3t7HDhwoBhrRkREREREVDibSLyio6Nz9WrZ29ujQoUKiI6OLnT5EydOoG7duggKCkLfvn1x8uRJS1WViIiIiIgoF5u41DApKQlKpTLXdKVSicTExAKXDQ0NRadOnVCpUiXExcUhKioKAwcOxNq1axESEmJ0nRQK43NWuVym97+p6xEEATKZYNK6XmS62Oj+F4Ts/02N/8vGkPb2fKzzoou/IAjS6xfas10UBOTeX+m9cbEQhJz/23Asn49DQTErJlaJtYntoSTGzVQCrLAvJTlupraJImyiyG3bmnGzQhys5b94G3fuplteJiv4u/ZFZ8i5nLnOt83JJhKv/IiiWOgHMDIyUu9906ZNERERgUWLFmHZsmVGbVcmE+Dp6WLUsjkplU4mrwMAHB3tACd7s6zrRebgYJf9wjH7f3PF/2VjSHuTYp3nCrLnKRQy2NnJzVm1kinHgV/+3P4qns1TyE2LhUJh23HMFYcCYlbcLBlrk9tDCY5bUSly7IvFjxMlOG7mOkYYtK2itm0rxs2acbC4Z/tib2/cKbiDA54tbwenl/g0RjeouSHnciXpfM8mEi+lUomkpKRc05OTk4s8JLyzszOaNGmC33//3ej6aLUikpLSjF5eLpdBqXRCUlI6NBqtyevJyMiCmK4yej0vOplMgIODHTIzs6DVihAysuAImBz/l40h7e35WOdFF3+1WousLI0Fa1wyCBotFAA0Gi20z+2v+ln7U2uMi4UgZJ8sqdUaiHmH2yY8H4eCYlZcrBFrk9tDCYybsXSx0BgZi6IoyXEztU0Ywti2bc24WSMO1iLTaCEHoFKpjToHycwUANhBpcpCeroNH/hNlJGRHYeCzuXMdb5dGKXSyeBeNZtIvPz9/XPdy6VSqXD79m1069atyOsTzfCtqVab/gfUaLRmWY8oivme5NJ/tNrsOMme/f3NFf+XjSHtTRfrvOjiL4qiWT6LJZ3wbBdFMY9jj/Te2FgI+a/bljwXhwJjVmysEGsT20PJjJtpRFh+X0p03Ew+RhjCuLZt1bhZJQ7WIUpxM+7cTRSz/15aLV7qcz9dHA05lytJ53sl56LHArzxxhs4duwYnj59Kk3bs2cPVCoVmjRpUqR1paWl4cCBAwgKCjJ3NYmIiIiIiPJkE4lXr1694ObmhuHDh+PQoUPYsmULvvzyS3Ts2FHvUsOJEyeiZs2a0vtTp05h2LBh2LRpE44dO4bffvsNffr0QXx8PEaMGFEcu0JERERERC8hm7jUUKlUYvXq1ZgyZQpGjRoFR0dHRERE4KOPPtIrp9VqodH8d+2vr68vVCoVZs+ejYSEBDg5OSEkJASTJ09GnTp1rL0bRERERET0krKJxAsAKleujKioqALLTJ8+HdOnT5feV6xYsdBliIiIiIiILM0mLjUkIiIiIiKyZUy8iIiIiIiILIyJFxERERERkYUx8SIiIiIiIrIwJl5EREREREQWxsSLiIiIiIjIwph4ERERERERWRgTLyIiIiIiIgtj4kVERERERGRhTLyIiIiIiIgsjIkXERERERGRhTHxIiIiIiIisjAmXkRERERERBbGxIuIiIiIiMjCmHgRERERERFZGBMvIiIiIiIiC2PiRUREREREZGFMvIiIiIiIiCyMiRcREREREZGFMfEiIiIiIiKyMCZeREREREREFsbEi4iIiIiIyMKYeBEREREREVmYzSReN2/exKBBg1C3bl2Eh4djypQpyMjIMGjZzZs3o23btggKCkJERAR27txp4doSERERERH9R1HcFTBEUlISBgwYgDJlymDevHl48uQJpk2bhoSEBMycObPAZXft2oXx48djyJAhaNiwIfbu3YsxY8bAzc0NjRo1stIeEBERERHRy8wmEq8ff/wRSUlJ2LJlC7y8vAAAcrkcH330EYYNGwZ/f/98l507dy7atm2LsWPHAgDCwsJw8+ZNzJs3j4kXERERERFZhU1canjw4EGEh4dLSRcAtGnTBvb29jhw4EC+y925cwcxMTGIiIjQmx4REYHz58/jyZMnFqszERERERGRjk0kXtHR0bl6tezt7VGhQgVER0fnu1xMTAwAoEqVKnrT/f39IYqiNJ+IiIiIiMiSbOJSw6SkJCiVylzTlUolEhMT811ON+/5Zd3d3fXmF5VMJsDLy8WoZQFAEHT1cIIoGr0aaT2O7w2EqNEYv6IXnAAAggBHUYQIQJDLAWTHn4quoPb2fKzzLPMs/u98Ww8atQkfAFshk0FQAGJWCJ7/wNs5ZP/2NeCrQGiyAoxavQDkG2tbkSsOBcSsOFk61ia3hxIaN2P8F4sa0GRVt+zGSnDczHGMMIRRbduKcbNWHKxCECDYAaJo3Cm4nV32/4MGKfAyn/o9O5Uo8FzaXOfbhZHJBIPL2kTilR9RFCEIhe/s82XEZ9E3ZNn81ieXG7dsTjKZeTocBVdXmF6bF5/w7J+OueL/sjGkvT0f67y4etiZqUa2Iv/9dXW36UOx2eSOw8vWRrKZ3h5enLhZ97NRcuNWso8R1otbyY5DUZl25ubqyjM/wLBzuZJ0vldyalIApVKJpKSkXNOTk5Pz7AnTya9nS7eugpYlIiIiIiIyF5tIvPz9/XPdy6VSqXD79u0CRzTU3dv1/L1c0dHREAQh171fRERERERElmATidcbb7yBY8eO4enTp9K0PXv2QKVSoUmTJvkuV758eVSpUgU7duzQm75t2zbUqVNHb5REIiIiIiIiS7GJxKtXr15wc3PD8OHDcejQIWzZsgVffvklOnbsqNfjNXHiRNSsWVNv2cjISOzcuRNz5szB8ePHMXXqVBw5cgSRkZHW3g0iIiIiInpJ2cRdikqlEqtXr8aUKVMwatQoODo6IiIiAh999JFeOa1WC81zQ7y0a9cOGRkZWLx4MaKiolCxYkXMmTOHD08mIiIiIiKrEUSxhI2dSkRERERE9IKxiUsNiYiIiIiIbBkTLyIiIiIiIgtj4kVERERERGRhTLyIiIiIiIgsjIkXERERERGRhTHxIiIiIiIisjAmXkRERERERBbGxMuKbt68iUGDBqFu3boIDw/HlClTkJGRUdzVsnm3bt3CpEmT0KlTJ9SsWRMRERF5ljtw4AA6d+6MoKAgtGrVCt9//72Va2r7du7cieHDh6NJkyaoW7cuOnbsiB9++AFarVavHGNtHocOHULfvn0RFhaG2rVro0WLFpg2bRqSk5P1yjHe5peamoo33ngDAQEBuHDhgt48xtt0mzZtQkBAQK5/M2fO1CvHWJvPxo0b8eabbyIoKAjh4eEYOnSo3nzG2jz69euXZ9sOCAjA9u3bpXKMt3ns3bsXPXr0QL169fD6669j5MiRiImJyVWupMRbUSxbfQklJSVhwIABKFOmDObNm4cnT55g2rRpSEhIyPVFQ0Vz/fp1HDhwAMHBwdBqtcjrmeBnzpzB8OHD0alTJ4wfPx5///03pkyZAnt7e/To0aMYam2bVq5ciTJlyuCTTz6Bt7c3jh8/jq+++gp37tzBuHHjADDW5pSYmIiQkBAMGDAASqUS169fx/z583H9+nWsWLECAONtKYsWLYJGo8k1nfE2r+XLl8PNzU16X6pUKek1Y20+8+fPx6pVqzB06FAEBwcjMTERhw4dkuYz1ubz2WefISUlRW/a6tWrsXv3boSHhwNgvM3l6NGjGDlyJN5880188MEHSEpKwoIFCzBw4EBs374drq6uAEpYvEWyiiVLlojBwcHi48ePpWm//fabWL16dfHGjRvFWDPbp9FopNfjxo0TO3TokKvMoEGDxO7du+tN+9///ic2bNhQb3kqWM72qzN16lQxKChIzMzMFEWRsba0DRs2iNWrVxcfPnwoiiLjbQk3btwQ69atK65fv16sXr26eP78eWke420ev/zyi1i9evU8jyk6jLV53LhxQwwMDBQPHTqUbxnG2rKaN28uDh48WHrPeJvHxIkTxWbNmolarVaadu7cObF69erin3/+KU0rSfHmpYZWcvDgQYSHh8PLy0ua1qZNG9jb2+PAgQPFWDPbJ5MV3IxVKhWOHTuGDh066E3v2LEj4uPjcfnyZUtW74WSs/3qBAYGIjMzEwkJCYy1FXh4eAAA1Go1420hX331FXr16oXKlSvrTWe8rYexNp9NmzahfPnyaNSoUZ7zGWvL+vvvv3H37l107NgRAONtTmq1Gi4uLhAEQZqWswcdKHnxZuJlJdHR0fD399ebZm9vjwoVKiA6OrqYavVyuH37NrKyslClShW96VWrVgUAxt9Ep0+fhoeHB7y9vRlrC9FoNMjMzMSlS5ewcOFCNGvWDGXLlmW8LWDXrl24evUqRowYkWse421+ERERCAwMRIsWLbBkyRLp8k7G2nzOnTuH6tWrY+HChQgPD0ft2rXRt29fXLlyBQBjbWnbtm2Dk5MTWrRoAYDxNqfu3bsjJiYGa9euRVJSEu7evYsZM2bA399fuqyzpMWb93hZSVJSEpRKZa7pSqUSiYmJxVCjl4cuvs/HX/ee8TfehQsXsGnTJowYMQJyuZyxtpBmzZohNjYWANC4cWPMnj0bANu2uaWnp2P69On48MMPpXsDcmK8zcfX1xejRo1CcHAwBEHAH3/8gW+//RaxsbGYNGkSY21G8fHxuHTpEq5fv47JkyfDzs5Oug9m9+7djLUFqdVq7Nq1Cy1atICzszMAHkfMKTQ0FAsWLMDYsWMxZcoUANkJ1YoVK2Bvbw+g5MWbiVcxE0VRr4uULCe/ODP+xomPj0dkZCSCgoIwePBgvXmMtXktXboUaWlpuHHjBhYtWoShQ4di5cqV0nzG2zy+++47eHt7o2vXrgWWY7xN17hxYzRu3Fh636hRIzg4OGD16tV6o+0x1qYTRRFpaWmYP38+qlWrBgCoVasWWrRogQ0bNqBevXoAGGtLOHLkCB4/fpznaMuMt+n+/vtvfPzxx+jWrRuaN2+OlJQULF68GIMHD8b69ev1fkArKfHmpYZWolQqkZSUlGt6cnJynj1hZD7u7u4Acv+qoft7MP5Fl5ycjMGDB8PR0RHfffcd7OzsADDWllKjRg3Uq1cPPXv2xIIFC3D8+HHs2bOH8Taje/fuYcWKFYiMjERKSgqSkpKQlpYGAEhLS0NqairjbWHt2rWDRqPBlStXGGszcnd3h4+Pj5R0AYCfnx+qVKmCGzduMNYWtG3bNnh4eOjdX8d4m8+UKVMQFhaGTz/9FOHh4WjVqhWWLVuGmzdvYuPGjQBKXryZeFmJv79/rutIVSoVbt++neveLzKvChUqwM7OLtdzHW7cuAEAjH8RZWZmYtiwYXj06BGWL18OT09PaR5jbXmBgYGQy+W4ffs2421Gd+/eRVZWFoYMGYLQ0FCEhoZKPS/9+/fHwIEDGW8rYqzNJ79YiaIImUzGWFtIRkYG9u3bh7Zt20o/TgJs2+YUHR2NGjVq6E3z8vKCn58fbt++DaDkxZuJl5W88cYbOHbsGJ4+fSpN27NnD1QqFZo0aVKMNXvx2dvbIywsDDt37tSbvm3bNvj6+qJmzZrFVDPbo1arMXr0aFy9ehXLly9H2bJl9eYz1pZ35swZaDQalCtXjvE2o8DAQKxZs0bv34QJEwAAkydPxmeffcZ4W9iOHTsgl8tRs2ZNxtqMmjZtikePHuHatWvStNjYWMTExCAgIICxtpA//vgDqamp0miGOoy3+ZQpUwaXLl3SmxYfH4+4uDjp/KTExduqg9e/xBITE8XGjRuLvXr1Eg8ePChu3rxZbNCggTh27NjirprNS0tLE3fu3Cnu3LlT7Nu3r9ikSRPpve4ZMX///bdYs2ZN8dNPPxWPHTsmLlq0SKxRo4b4008/FXPtbcv//d//idWrVxeXLVsmnjlzRu9fcnKyKIqMtTmNGDFC/O6778Q//vhDPHr0qLhixQrx9ddfFzt27Cg9N43xtpxjx47leo4X420e7777rrh06VLxzz//FP/880/x//7v/8SAgADxq6++ksow1uahVqvFLl26iK1btxa3b98u7tmzR+zcubPYuHFjMTU1VRRFxtoShg4dKjZt2lTvGVM6jLd5rF27Vqxevbo4efJk8fDhw+KOHTvETp06iaGhoWJsbKxUriTFWxBFUbRuqvfyunnzJqZMmYLTp0/D0dERERER+Oijj+Do6FjcVbNpd+/elYZpfd6aNWvQoEEDAMCBAwcwe/ZsREdHo3Tp0hg4cCD69OljzaravObNm+PevXt5zmOszW/p0qXYsWMHbt++DVEUUbZsWbRq1QqDBg3Su2mY8baM48ePo3///vj5558RFBQkTWe8TTdlyhQcOnQIDx8+hFarRaVKldCjRw/069dP72Z3xto8Hj9+jKlTp+LAgQNQq9UIDQ3FhAkT9IbYZqzNJzExEQ0bNsSAAQPw8ccf51mG8TadKIr46aef8MMPP+D27dtwdnZGUFAQxowZg4CAAL2yJSXeTLyIiIiIiIgsjPd4ERERERERWRgTLyIiIiIiIgtj4kVERERERGRhTLyIiIiIiIgsjIkXERERERGRhTHxIiIiIiIisjAmXkRERERERBbGxIuIiIiIiMjCmHgREREVs7fffhsBAQE4d+6cVbe7dOlSBAQEYO7cuVbdLhHRy0gQRVEs7koQERHp3LhxAz/++CNOnDiBe/fuITMzEx4eHvDx8YG/vz/q16+PsLAwVK5cucD1/PHHH9i1axfOnj2L+Ph4aLVaeHt7o3bt2mjRogUiIiIgl8vzXT42NhZ79+7F8ePHceXKFcTGxgIAfHx8ULduXfTo0QPh4eEm7+/vv/+OyMhINGnSBEuXLs01/8KFC5g3bx7+/vtvqNVqVKtWDYMGDUK7du3yXeexY8cwYMAANGvWDIsXL863XGpqKlq0aIGMjAz8/vvvKFWqlMn7Q0REeWPiRUREJcby5csxZ84cqNVqaZpSqYRKpUJGRoY07bXXXsPatWvzXMedO3cwduxYvd4jR0dHyOVypKamStMqV66M2bNno2bNmrnW8eDBAzRr1gw5vyKdnJwgiqJePbp164Yvv/yywASuIFlZWejQoQNu3bqFjRs3ok6dOnrzz507h379+iEzMxNyuRwKhQKZmZkAgEmTJqFPnz651qlSqdCxY0fExcVh+/btKFOmTIF1WLp0KWbNmoWuXbti2rRpRu0HEREVjpcaEhFRibB792588803UKvVCA0NxYoVK3D+/HmcPHkS586dw8GDBzF79my0adMGdnZ2ea4jOjoab731Fs6dOwdHR0eMGDECe/fuxblz5/D333/j6NGj+PTTT+Hh4YGbN2+iT58+OHXqVK71aDQaiKKI8PBwzJgxAwcPHsTZs2dx5swZbN++HS1atAAA/PLLL5g/f75J+3zr1i3Url07V9IFAF9//TUyMzPx5ptv4tSpUzhz5gwmTJgAAJg1axZSUlJyLfPdd9/h33//xejRowtNugCge/fuUCgU+O233xAXF2f0vhARUcHY40VERCVCr169cObMGVSvXh2bN2+GQqHIt2xGRgYcHR1zTevevTuuX78OV1dXrFy5Ms9kBsjuFevfvz/u378PX19f/Pbbb/Dy8pLmJycn4/bt26hVq1aey4uiiMGDB+PQoUNwdnbGsWPH4ODgUOR97tevH06cOIGJEydiwIABevPS09NRr149CIKAEydOwNXVVZrXpUsXXL58GVFRUWjUqJE0PTo6Gp06dUL16tWxceNGg3vi3n//ffz555/44IMPMGzYsCLvBxERFY49XkREZFH9+vVDQEAA5s+fj6ysLKxYsQJdu3ZF/fr1ERAQgOPHjwMArl69CgB44403Cky6AORKugBg48aNuH79OgDgf//7X75JFwCUL18eX3/9NQAgPj4ey5Yt05vv5uaWb9IFAIIgoFu3bgCAtLQ0REdHF1jfvPz77784ceIEBEFAhw4dcs1PSkqCVquFp6enXtIFABUrVgQAPHnyRJomiiImTZoErVZb5MsfIyIiAGTHkIiILIOJFxERWUVmZib69euHGTNm4J9//oFMlvdX0MOHD41a/w8//AAgO6nq3LlzoeVDQ0PRoEEDAMBPP/2kd1+ZIXL2cGk0miItCwCHDh0CAFSqVAk+Pj655iuVSshkMjx9+jTXJYW3b98GAL1euo0bN+LUqVPo379/gUljXkJDQwEA9+7dMyqJJCKiwjHxIiIiq/j+++/xzz//YNq0aTh9+jROnDiBY8eOISAgAAAQFBQEANi1axe2bt0KrVZr8Lrj4uIQExMDAGjVqhUEQTBoudatWwMAUlJScOnSpaLsDk6cOAEAsLOzK3SExbycPHkSAPLtmXNyckJISAg0Gg2++OILpKenQ6PRYM2aNbh06RKcnZ1Rt25dAMCjR48wc+ZMlClTBpGRkUWuS+nSpeHn56e3X0REZF5MvIiIyCrS0tKk0fN0lwp6enrCw8MDADBq1CgoFAqo1Wp89NFHeOONN/DBBx9g+fLlOHbsGNLS0vJd940bN6TXgYGBBtepRo0a0utr164ZvNydO3fw448/AgDat2+f61JAQ5w/fz5XHZ738ccfw97eHr/++iteffVVhISE4KuvvgIAfPDBB9J2p06disTERHz22WdwdnYucl0ASKM7nj171qjliYioYEy8iIjIKqpVq4bmzZvnO/+1117D8uXLpd6j+Ph47Ny5E9988w0GDBiA1157DUOGDJF6inJKSEiQXusSOUN4enrmuY6CZGRkYPTo0UhPT4eHhwfGjh1r8PZ0RFFEfHw8AP3LBZ8XEhKC77//Ho0aNZKS1Vq1amH27NnSYBwHDx7E9u3b0bZtWzRt2hQAsHbtWrRv3x61a9dG48aNMWXKlDxHQMxJFwuObEhEZBkF371MRERkJvXq1Su0THh4OHbs2IFTp07h8OHDOHfuHK5evYqEhARkZWXhwIEDOHDgAIYPH47Ro0fnuQ5DLzM0hlqtxtixY3Hp0iXY2dlh1qxZRj10OCkpSbqnzN3dvcCyderUQVRUVJ7z0tPTMXnyZLi5ueHTTz8FAMyYMQMrVqxAqVKlEBERgStXrmDt2rW4ePEi1q1bl+/AJbp65Bywg4iIzIeJFxERWUVBPTs5yWQyvPbaa3jttdekadHR0di+fTtWrlyJtLQ0LFq0CHXq1EGzZs0A6PdyPX361OA65SxbWE+ZRqPBxx9/jL1790KhUGDmzJl6Q7kXhe4hyABgb29v1DoAYMGCBbh79y4mT54MPz8/xMTEYOXKlfDx8cHmzZvh7e2NrKwsvPvuuzhx4gQ2bdqEnj175rkuXY9azroREZH58FJDIiKyiqIMb/48f39/REZGYtGiRVKPVs6hz/39/aXXly9fNni9V65ckV5Xq1Yt33K6pGvHjh2Qy+X45ptv0LZt26Lsgp6cSV5iYqJR67h69SpWrVqFkJAQvPXWWwCAffv2QRRFvPnmm/D29gaQPfiH7rLEvXv35rs+3aWWRblUk4iIDMfEi4iIbEZ4eLj0DKubN29K00uVKoUqVaoAAPbs2QNRFA1a3+7duwEArq6uqF27dp5lNBoNPvroI2zfvl1Kutq3b2/KbsDe3l4aGMOYxEur1WLSpEkQBAFffvmllIzeuXMHQPaQ+jlVqlRJb35edPUwtGeSiIiKhokXERHZFN2ofc9fote7d28AwN27d7Fly5ZC13Py5Elp6PSePXvmee+TLunK2dOV18OOjVG1alUABSdD+fnhhx9w7tw5DBo0KM+euucvF8zIyABQ8P1vd+/eBaDfe0hERObDxIuIiEqEw4cPF9pTdfXqVVy9ehXAf8Of6/Ts2VPq9ZoyZYo0XHte7ty5g08++QQA4OPjg0GDBuUqo9FoMHbsWOzYsUO6p8tcSRcA1K9fHwBw4cKFIi0XGxuLOXPmoGLFihg+fLjevHLlygFArn0/d+6c3vznqVQqKa66hykTEZF5MfEiIqISYezYsWjbti0WLlyI8+fPQ6VSSfPi4+OxatUqvPPOO9BqtVAoFNJ9SzqOjo6YP38+PD09kZKSgn79+mH+/Pm4d++eVObx48dYs2YNunfvjvv378PJyQmzZ8+Gj4+P3ro0Gg0++eQT7Ny5U0q6TL288Hm6wUMuXrwIjUZj8HJffvklUlJSMHnyZDg4OOjNa9GiBYDsyy13794NURRx/fp1LFmyRG/+8y5fvoysrCwoFAqDRp8kIqKiE0RDL4QnIiIyQr9+/XDixAmMHDkSo0aNyrdco0aNpGdbAdmjG7q5uSE9PV0vCXNxccHUqVPzHdzi9u3bGDNmDC5evChNc3Jyglwu13uWVaVKlTBz5kwEBQXlWsfJkyfRt29fANmDUxQ25Punn35a5MRMpVKhSZMmePLkCVasWIGGDRsWuszevXsxYsQIdO7cGTNmzMizzJQpU7B27VoA2cmo7jLD4OBgfP/997Czs8u1zOzZs7FkyRK0bNkSCxcuLNJ+EBGRYTicPBERlQi7du3C4cOHcfz4cVy+fBm3b99GUlISBEGAj48PqlSpgoYNG6J79+65eqhyqlChAn7++Wfs27cPu3btwtmzZ/H48WNkZWX9f3t3qKpIFAZw/OMGMVosomZB8CmUMZhEfA6bwepjWC+IVYugWHwBQfABNFpNgm7SdO9dlt2jYX+/NGFgThr48805E6VSKer1ejSbzeh0Ot/+0+p2uz2vr9drnM/nH9f+iJs/kcvlotvtxmQyifl8/tvwulwuMR6Po1AoxHA4/Pa+0WgU5XI5ptNpnE6nKBaLkWVZDAaDL6Prfr/HYrGIiHiejgjAv2fiBQBvcjweI8uyyOfzsd1unweHvNJjuletVmO5XMbHh10IACl4uwLAm1Qqlej1enG5XOLz8/Mta3js/xoMBqILICETLwB4o/P5HK1WK/L5fKzX65dOvXa7XfT7/Wg0GjGbzX48bh6AvyO8AODNVqtVHA6HaLfbX/6XK5XNZhP7/T5arVbUarWXPRfgfyS8AAAAEvMxNwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYr8APngCW3DPvscAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpre = df[(df['ROSC-CPC12'] == 0) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Pre-ROSC Medians (-5,-2)'].tolist()\n",
    "xpost = df[(df['ROSC-CPC12'] == 0) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Post-ROSC Medians (0,5)'].tolist()\n",
    "\n",
    "# Plot overlapping histograms\n",
    "sns.histplot(xpre, kde=False, color='red', label='Pre-ROSC Medians', alpha=0.5, bins=8)\n",
    "sns.histplot(xpost, kde=False, color='blue', label='Post-ROSC Medians', alpha=0.5, bins=8)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('Not, (0,5) min interval', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798e2ed7-9d0c-42e9-b8a8-03468b31eaf5",
   "metadata": {},
   "source": [
    "## (0,2) min post-ROSC interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e8604ddd-1e16-451b-9fa2-2d593254d266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.5 6.568643463614326e-07\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC'] == 1) & (np.isnan(df['Difference (0,2) (rSO2 %)']) == False)]['Difference (0,2) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ac0488dd-cee7-4cea-89f1-96fffc588539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.125\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (np.isnan(df['Difference (0,2) (rSO2 %)']) == False)]['Difference (0,2) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "405bd153-a40d-437a-8743-0dbeaee786d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.5 3.6636103910367503e-06\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 0) & (np.isnan(df['Difference (0,2) (rSO2 %)']) == False)]['Difference (0,2) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f01e1d5-8be2-49cc-a1d8-7fefcd199365",
   "metadata": {},
   "source": [
    "## (0,3) min post-ROSC interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8d745a2c-2523-4d8a-86ff-342c308678fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.0 1.83499651029706e-08\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC'] == 1) & (np.isnan(df['Difference (0,3) (rSO2 %)']) == False)]['Difference (0,3) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "57e0f574-6721-4e06-b9d1-3e0d6cda0eff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.125\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (np.isnan(df['Difference (0,3) (rSO2 %)']) == False)]['Difference (0,3) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "13a65210-79d8-4751-902c-416e69571c6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.0 2.935994416475296e-07\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 0) & (np.isnan(df['Difference (0,3) (rSO2 %)']) == False)]['Difference (0,3) (rSO2 %)'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca93e3f-5768-4777-873a-767ce176ee46",
   "metadata": {},
   "source": [
    "## Diff over Entire Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "564ef8e7-5cff-48dd-8d36-ce8bba6c4a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[np.isnan(df['Signal Diff']) == False]['Signal Diff'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "009b02ca-c5b0-48fe-93b6-80302aa1e044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.25"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(np.isnan(df['Signal Diff']) == False) & (df['ROSC'] == 0)]['Signal Diff'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9a12a994-7f5c-4f6e-8082-50ed44b9f774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "517.0 7.62806277052911e-06 73 5.0 8.636986301369863\n"
     ]
    }
   ],
   "source": [
    "x = df[np.isnan(df['Signal Diff']) == False]['Signal Diff'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4fc2e2e9-de4c-4f99-b48e-5c37c5f74ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0 1.0011717677116394e-08 33 22.0 23.303030303030305\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff']) == False) & (df['ROSC'] == 1)]['Signal Diff'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dcc12546-e7e9-4d58-84a0-9170a5b907cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "316.0 0.300756142061227 40 1.0 -3.4625\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff']) == False) & (df['ROSC'] != 1)]['Signal Diff'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "299559e2-d3e6-4002-8924-1812522849d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.5 2 18.5 18.5\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff, min 1']) == False) & (df['ROSC-CPC12'] == 1)]['Signal Diff, min 1'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "82a77604-04ae-42ef-95f9-931a0c7e2b39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "407.5 6.583128361930571e-05 69 4.0 11.681159420289855\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff, min 1']) == False) & (df['ROSC-CPC12'] != 1)]['Signal Diff, min 1'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8100d914-6d33-4e6e-a4e1-158dc1d36a65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "637ca889-9c14-4ea4-ade8-9d80930687e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c0e93f-bff3-459d-a3cc-209edea10289",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d5748a20-2695-4e55-b8a4-9ca66a03108d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xstart = df[(np.isnan(df['Start Medians']) == False) & (np.isnan(df['End Medians']) == False)]['Start Medians'].tolist()\n",
    "xend = df[(np.isnan(df['Start Medians']) == False) & (np.isnan(df['End Medians']) == False)]['End Medians'].tolist()\n",
    "\n",
    "# Plot overlapping histograms\n",
    "sns.histplot(xstart, kde=False, color='red', label='Signal Start Medians', alpha=0.5, bins=10)\n",
    "sns.histplot(xend, kde=False, color='blue', label='Signal End Medians', alpha=0.5, bins=10)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('All Cases, Start vs. End Signal Medians', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "17de163b-8312-48a9-b50f-b2111dec0d49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xstart = df[(np.isnan(df['Start Medians']) == False) & (np.isnan(df['End Medians']) == False) & (df['ROSC'] == 1)]['Start Medians'].tolist()\n",
    "xend = df[(np.isnan(df['Start Medians']) == False) & (np.isnan(df['End Medians']) == False) & (df['ROSC'] == 1)]['End Medians'].tolist()\n",
    "\n",
    "# Plot overlapping histograms\n",
    "sns.histplot(xstart, kde=False, color='red', label='Signal Start Medians', alpha=0.5, bins=5)\n",
    "sns.histplot(xend, kde=False, color='blue', label='Signal End Medians', alpha=0.5, bins=5)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('ROSC, Start vs. End Signal Medians', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "219fb634-d03c-4c0b-b665-0c422a105c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/cpr/miniconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xstart = df[(np.isnan(df['Start Medians']) == False) & (np.isnan(df['End Medians']) == False) & (df['ROSC'] != 1)]['Start Medians'].tolist()\n",
    "xend = df[(np.isnan(df['Start Medians']) == False) & (np.isnan(df['End Medians']) == False) & (df['ROSC'] != 1)]['End Medians'].tolist()\n",
    "\n",
    "# Plot overlapping histograms\n",
    "sns.histplot(xstart, kde=False, color='red', label='Signal Start Medians', alpha=0.5, bins=10)\n",
    "sns.histplot(xend, kde=False, color='blue', label='Signal End Medians', alpha=0.5, bins=10)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('rSO2 (%)', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('No ROSC, Start vs. End Signal Medians', fontsize=24)\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba1f79c-4b98-4fdd-aea5-00ffdfc2c906",
   "metadata": {},
   "source": [
    "### Diff over Entire Signal, start at min 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f7295969-c9b2-4476-9a7d-5f7253629d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49.5 5.5925920605659485e-05 30 11.5 14.366666666666667\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff, min 1']) == False) & (df['Witnessed'] == 1)]['Signal Diff, min 1'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0a96efd6-54bf-4147-b68e-16ebc4026f13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0 7.62939453125e-05 18 18.0 21.22222222222222\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff, min 1']) == False) & (df['Witnessed'] == 1) & (df['ROSC'] == 1)]['Signal Diff, min 1'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6b8b1385-1132-48d9-b3dc-3f44d6d27d09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.0 0.38037109375 12 1.0 4.083333333333333\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff, min 1']) == False) & (df['Witnessed'] == 1) & (df['ROSC'] != 1)]['Signal Diff, min 1'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90759917-d3f5-4811-b424-f91b7747f9ca",
   "metadata": {},
   "source": [
    "### Diff over Entire Signal, start at min 0, witnessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "46d135c1-560b-4218-8d28-be223a2aefe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.0 2.4959444999694824e-07 32 13.5 19.0\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff']) == False) & (df['Witnessed'] == 1)]['Signal Diff'].tolist()\n",
    "res = wilcox(x)\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "56da5b63-338d-436e-988f-ba8911e75fcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1.9073486328125e-06 20 25.0 26.85\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff']) == False) & (df['Witnessed'] == 1) & (df['ROSC'] == 1)]['Signal Diff'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fb614884-ef79-421d-92d6-4376a2638499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.0 0.12939453125 12 2.0 5.916666666666667\n"
     ]
    }
   ],
   "source": [
    "x = df[(np.isnan(df['Signal Diff']) == False) & (df['Witnessed'] == 1) & (df['ROSC'] != 1)]['Signal Diff'].tolist()\n",
    "res = wilcox(x, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue, len(x), np.median(x), np.mean(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9be5a7-c622-4fe3-a05e-784120208221",
   "metadata": {},
   "source": [
    "## Mann-Whitney U Test - rank-sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7642bcb2-10bf-4f91-9469-fbdbb1da1b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mannwhitneyu as mwu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "459c35a0-9f35-4491-9900-0fc24dcf1c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122.0 0.0919389021741151\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)']\n",
    "y = df[(df['ROSC-CPC12'] == 0) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)']\n",
    "res = mwu(x, y, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "84667eaa-9da2-49dd-9e93-269487d30e02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.5 0.10071318010942364\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)']\n",
    "y = df[(df['ROSC-CPC12'] == 0) & (df['Witnessed'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)]['Difference (0,5) (rSO2 %)']\n",
    "res = mwu(x, y, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "512913c2-0179-46a7-ad3d-41522f710502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49.0 0.8263824698151412\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (np.isnan(df['Signal Diff']) == False)]['Signal Diff']\n",
    "y = df[(df['ROSC-CPC12'] == 0) & (np.isnan(df['Signal Diff']) == False)]['Signal Diff']\n",
    "res = mwu(x, y, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a809adf8-33ec-406b-b758-15e88a176e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.5 1.0\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Signal Diff']) == False)]['Signal Diff']\n",
    "y = df[(df['ROSC-CPC12'] == 0) & (df['Witnessed'] == 1) & (np.isnan(df['Signal Diff']) == False)]['Signal Diff']\n",
    "res = mwu(x, y, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bee823e1-84ca-4b8c-abdd-635e84541813",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.5 0.9378849357808439\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (np.isnan(df['Signal Diff, min 1']) == False)]['Signal Diff, min 1']\n",
    "y = df[(df['ROSC-CPC12'] == 0) & (np.isnan(df['Signal Diff, min 1']) == False)]['Signal Diff, min 1']\n",
    "res = mwu(x, y, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5d0108a0-9529-42fb-8c6d-fc0df64dedea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.0 0.9439127581689829\n"
     ]
    }
   ],
   "source": [
    "x = df[(df['ROSC-CPC12'] == 1) & (df['Witnessed'] == 1) & (np.isnan(df['Signal Diff, min 1']) == False)]['Signal Diff, min 1']\n",
    "y = df[(df['ROSC-CPC12'] == 0) & (df['Witnessed'] == 1) & (np.isnan(df['Signal Diff, min 1']) == False)]['Signal Diff, min 1']\n",
    "res = mwu(x, y, nan_policy='omit')\n",
    "print(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb904f1-f919-44e9-bb46-b138e9584c05",
   "metadata": {},
   "source": [
    "# 5 - FIGURE 4\n",
    "Logistic regression and ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e91468b-2c08-4e19-a86f-77357dac8e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import statsmodels.api as sm\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(5, 4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6ec46ddb-0b66-4128-b525-8f75db4570ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfroscdiff = df[(df['ROSC'] == 1) & (np.isnan(df['Difference (0,5) (rSO2 %)']) == False)][['CASSID', 'Witnessed', 'Signal Diff',\n",
    "                                                                                     'Difference (0,5) (rSO2 %)', 'ROSC-CPC12']].copy()\n",
    "dfsig = df[np.isnan(df['Signal Diff']) == False][['CASSID', 'Witnessed', 'Signal Diff', 'ROSC', 'ROSC-CPC12']].copy()\n",
    "dfsigsurv = df[(np.isnan(df['Signal Diff']) == False) & (np.isnan(df['ROSC-CPC12']) == False)][['CASSID', 'Witnessed', 'Signal Diff', 'ROSC', 'ROSC-CPC12']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7b6c350-26b5-44ec-bb4f-aef256ae2651",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.0\n",
      "13.0\n",
      "34.0\n"
     ]
    }
   ],
   "source": [
    "print(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 1]['Difference (0,5) (rSO2 %)'].median())\n",
    "print(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 1]['Difference (0,5) (rSO2 %)'].quantile(0.25))\n",
    "print(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 1]['Difference (0,5) (rSO2 %)'].quantile(0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "030ab2d1-21a5-4e19-b45b-38f330a9b79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc827cec-e3b9-4fee-b542-5dd5498ab1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "4.0\n",
      "19.0\n"
     ]
    }
   ],
   "source": [
    "print(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 0]['Difference (0,5) (rSO2 %)'].median())\n",
    "print(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 0]['Difference (0,5) (rSO2 %)'].quantile(0.25))\n",
    "print(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 0]['Difference (0,5) (rSO2 %)'].quantile(0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "881d5072-391d-40c6-965c-ca379c133781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfroscdiff[dfroscdiff['ROSC-CPC12'] == 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804225f7-c4ee-4f06-9c9a-6b0e4cb04d71",
   "metadata": {},
   "source": [
    "### Cut-point Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "869983fc-3730-4e32-9b29-c2fe7003aa15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    34.0\n",
       "44    29.0\n",
       "45    13.0\n",
       "65     6.0\n",
       "75    39.0\n",
       "Name: Difference (0,5) (rSO2 %), dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfroscdiff[dfroscdiff['ROSC-CPC12'] == 1]['Difference (0,5) (rSO2 %)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "02197303-e6fb-4f75-9edf-22442a0ab9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     21.0\n",
       "6      7.0\n",
       "7     15.0\n",
       "10    -7.0\n",
       "11    10.0\n",
       "14    -3.0\n",
       "17    19.0\n",
       "18    10.0\n",
       "22     6.0\n",
       "31    21.0\n",
       "32    15.0\n",
       "38    -1.0\n",
       "39     7.0\n",
       "40    20.0\n",
       "41    12.0\n",
       "43     0.0\n",
       "46    -3.0\n",
       "47    44.0\n",
       "58    23.0\n",
       "63     6.0\n",
       "66    -2.0\n",
       "69    28.0\n",
       "71    14.0\n",
       "72     1.0\n",
       "74    19.0\n",
       "77    22.0\n",
       "79     4.0\n",
       "80     8.0\n",
       "81    41.0\n",
       "83    14.0\n",
       "85     9.0\n",
       "86     3.0\n",
       "90     9.0\n",
       "Name: Difference (0,5) (rSO2 %), dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfroscdiff[dfroscdiff['ROSC-CPC12'] == 0]['Difference (0,5) (rSO2 %)']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453e90c4-459c-454f-a6fa-d20f68b312aa",
   "metadata": {},
   "source": [
    "### Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "960c1f81-9154-4620-82e3-2d651114b230",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_theme(rc={'figure.figsize':(5,4)})\n",
    "sns.set_context(\"notebook\", font_scale=1.2)\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "626eb1aa-ecc3-4558-bbfe-c793864a3834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VL7/8ss4/Tvfs2aP5XH700UcMGTKEy5cv4+bmxoQJE7SOVX8ui/L61Z8T9WcSHn+nffXVV9jZ2bF161Zee+01PDw86Nq1K7NmzeLgwYNa1yjLz121vdMubpWTq6trrm3qN0B8fHypxARofp0+6fr168TFxdG0aVN++eWXPM8zMzPj+vXrRXqO+vXrs2LFCq5du8Zff/3F+fPnOXfuHGFhYYSFhbF27VpWrFhBw4YN873G9evXSUtLw9XVFUtLy1z73dzcNNVDeSlOeT569IilS5dy8OBBIiMjSUlJ0dr/5IerLERFRfHTTz9pbbOxsWHFihW5hgaqq82f7HlakP8eV9zzC/NkNX55yut9DI/fF02bNmX//v3Ex8djY2MDwJYtW8jOztaqplX3lK9ZsyYrVqzI83omJial1nnOzc0tz+aMLl26cPz4cS5evJir6jiv1/nPP/9o+lrkVaOnbq9/8vN68eJFDAwM8hxCWNTqakBzd/ffH/5loW3btrm25fUZDg8PB3Lf/QJYWFjg7Oys6QdUHHv37mXv3r1a23r16sXixYtzNZcU53OlPua/xw4ZMgR3d3eOHTvGqVOnCA8P59SpU4SGhhIaGoq3tzcLFixApVKV6eeu2ibt4sqr84H6jreoncCKIq/hZnFxccDj9pT/Jo8nFeUO+UktWrTQ6h167do13nvvPc6cOcPnn3/Ozz//nO+56ra72rVr57k/v+1qRS3PhIQExo4dS2RkpKajm42NDUZGRiQkJLBy5cpS6YxUkCfHscbFxbFz507mz5/PjBkz2Lhxo9ZrrVOnDvD4h0Z6enquu8MnpaWlaf626uYA9f+f7OBYEra2thgbG5OZmUlMTEy5jVwoaNjk6NGj+f7779m+fbvmznbz5s0YGxszYsQIzXEJCQkoikJsbGyB7/uyjlm9Pa+e+gV9Xv/5558Ca+Ge/LwmJiZiY2OTZ9u8+j1VFOrPZV6dnkpbXj/W8/oMq2MqrHyL6/PPP8fLy4vs7Gxu3brFd999x86dO/n000/55JNPtI5Vl6G6eaAgMTExWuc8ydjYmF69etGrVy/gcfPkrl27eO+99wgMDGTAgAF4eHiU6edOknYFk9cvQXWCGzhwYJl+ebVo0YIvv/ySgQMHcuTIkQKPVX9gHz58mOf+/LYXV0BAAJGRkcycOTNXu9uZM2dYuXJlqTxPUdna2jJ+/Hiys7P55JNP+Oijj7Tupho0aED9+vW5e/cux44dK/CO5/jx42RlZdGgQQNNFZr6buT8+fOayVhKwsjIiA4dOnDixAmOHDlSrC8P9Xsxrx766i/iws7Ny5gxY/jhhx/YtGkTTz/9NBcuXCAiIgJ3d3etIUzq91jr1q1zVaeXBXUVan7b80pSBX1en3nmGebOnVuk57aystJUqf43caubTIp6HXiceIoykVR5UJdbYeWrK0NDQ5o1a8Y333zD3bt32bBhA/369dPqya/+XB07dozs7OxcHdWe9PfffwPQqVOnIj33sGHDiIiI4Ndff+Xo0aN4eHiU6HNXmGrbpl2W1FVspTUcqXnz5lhbW3P27FlNO0lZUfcmL6x6p3nz5piZmXH58uU870DUHT9K6ubNmwAMGjQo174TJ06UynPoYvz48bRq1YrQ0NBcr1XdcWjx4sX5lmNOTg6LFy8GYNy4cZrtjRo1okePHqSnp+Pn51doHEWpZVBff9myZaSmphb5euqq67zuTs6fP1/o8+anQYMGdOnShbCwMP79919NQn6yahwevxdbtWrFlStXNHevZen06dN51pqpq27/23adn3bt2mFgYJBnG3h+WrduTU5OTp6fm+JUHas7ZBa1n4eBgUGZD5tUNyHl9dqSk5O5dOlSqTyPoaEh7733HvC4Z/uTr6tLly40adKEe/fuafoP5OXKlSvs3r1bq32+KPL63tT1c1cYSdplQN25qChVMUVhZGTEpEmTuH//PvPnzyctLS3XMffu3ePq1auFXuv27dusXLkyzzslRVH49ddfAfLsxPIkExMThg0bRmJiouYctUuXLml6ApeUul39v2NRL168yG+//Vasa6nHgxb0oS0qQ0NDzZ3/t99+q7Xv2WefpUWLFpw6dYr33nsv198rLS2NefPmcerUKRwdHXP1fJ43bx6WlpYsWbKEZcuW5RqzDI97qb/++uucOXOm0FhHjBhBr169uHHjBjNmzMizD0BGRgZr1qxh4cKFmm3q9tqgoCCtGO7evVtg00lRqL8QN27cyPbt27G1taVfv365jnvmmWfIzMzk3XffzXP8b16TWqjHL+c3QiQ/N27c0IxjVtuzZw/Hjx+nSZMmeQ75ykvt2rUZOXIk58+f5+eff87z73fr1i1u376teawuj++++4709HTN9ri4uFyfr4KMGTMGS0tL1q5dm2eS/G+zi62tbak1xeTHw8NDM9Tvvwn6119/zXdcty7at29P//79+ffff7W+gwwNDfnwww8xMDBg/vz57N69O9e5165d4+WXXyYzM5OXX35Zq0/Ptm3b+Ouvv/L8UXf//n1N/50n3yO6fu4KU22rxwv6QHt4eJRo7vHu3buzc+dOZs2aRZ8+fTA1NaVBgwaF9qguyIwZM7h06RLr169n//79dOvWjXr16vHw4UNu3rzJ6dOnef3112nZsmWB10lKSmLBggV8+eWXdOrUCUdHRywsLHj48CFHjx7l9u3b1K5du0hT6r355pscPXoUPz8/zp07R8eOHbl//z4hISH07duXPXv2lLhD1ejRo1m6dCmff/655svz5s2bHDhwgIEDB7Jjx44iXyu/MZy6GjRoEC4uLpw4cYJDhw7Ru3dv4PGvbj8/P15++WUCAwP5888/6dOnD3Xq1OHBgwccPHiQ+/fv4+LiwuLFi3NNvtKiRQuWLl3KrFmz+OKLL1i5ciXdu3enbt26pKSkcOnSJU2ynj59eqFxGhgY8P333/POO++wd+9ePDw86N69O82bN9cMOTty5AixsbFavZ3btWun6YTl4+NDt27dePDgAfv376dXr14l+lE6aNAgPv74Y1auXElmZiaTJ0/Osz137NixXLhwgbVr1zJw4EB69epF/fr1iY+PJzIykhMnTuDl5aXVhqnr37l3794sXLiQP//8E2dnZ804bVNTUxYsWJDvmPu8fPDBB9y8eZMffviBLVu20KlTJ+zs7Lh37x7Xrl3jn3/+4ZtvvtH0sB8xYgQ7duxg3759jBgxAnd3d7Kysti5cydt27bl1q1bRXreWrVq8fXXX/PKK68wadIk+vbti6OjI4mJiVy+fJno6Gj27dunOb579+5s376dl19+mdatW2NoaEjnzp0L/dFeHJaWlnz44Ye8/fbbjB8/Xmuc9qVLlzTvseKUb0FeeeUVDhw4wM8//8zIkSM1Q1l79uzJV199xbvvvsvMmTNp164dnTp10kxjevjwYTIzM3nuuef4v//7P61rhoWFsXLlSurUqUOnTp00CT0yMpKDBw+SlpaGu7u71qQtun7uClNtk3ZBbcMODg4lSto+Pj7cuXOH7du34+fnR1ZWFl26dClR0jY2NuaXX35h8+bNBAcHc+DAAVJSUqhZsyYNGzbk1VdfZeTIkYVep0WLFvz8888cPnyYsLAwduzYQXx8PGZmZjRp0oSXXnqJqVOnFml6RDs7O9avX88333zDwYMHCQsLo1mzZpppO/fs2VPiNtl69eqxZs0avvrqK06dOqWZXObDDz+ke/fuxUraERERWFhY5HlHpwuVSsUrr7zCyy+/zHfffadJ2vC4Cnjjxo0EBwdrvowTExOxtLTE2dmZV155BU9Pz3wnBenQoQM7d+4kICCAvXv3cuDAARISEjR/p2effZZx48YVecEQS0tLfvnlFw4fPkxwcDBnzpzhyJEjKIpC3bp16dGjB6NHj87VBv/LL7+waNEidu/ezapVq2jatClvv/02PXv2JCQkROeyq1GjBoMHD9ZUjRf02fjwww/p06cP69ev5++//9Z02qpfvz7PPfecZhYstYiICAwMDBg6dGixYmrfvj3/93//x/fff8/q1as1c/O/9tpr+S4ikR9LS0tWrVqFv78/27ZtIzQ0lPT0dOzs7GjSpAlz587VmkFLPRRzyZIlBAcHs3r1aurWrYu3tzf/93//l2dP7fz069ePwMBAfv/9d44cOcLhw4extramefPmvPjii1rHvvfee6hUKo4cOcKBAwfIyclh5syZpZq0AUaOHIm1tTW//vorO3bswMTEhKeeeor169fz5ZdfAnl3TtVF69atGThwIKGhoWzYsEFrJsfhw4fj5uamWTDE399fs2DI8OHDmThxYp5/62nTptG0aVP+/vtvLl++zOHDh8nIyMDW1pYuXbowYsQIRo4cmesmRdfPXUFUir7GhIgq7dtvv2Xx4sX4+flpJTN9SUhIoGvXrjz77LO88847+g5HlBF1ou3WrRvff/99kc5Rr+aWV2dHUbays7Px8PAgMzOzzOdbqCqkTVuUiHp4xJMuX77MypUrNb9CK4KTJ09iZGTEs88+q+9QRBmKiIggLi4u1x2l0K+EhIRcnbHUfWju3LmTZ0dTkbcKWT2+ZMkSLl68yMWLF7l16xYGBgZcvHix2Nd59OgR3333HXv37iUuLg4HBwe8vb3zXDVJ6Mbb25smTZrQqlUrzM3NuXnzJgcPHiQnJyfPWaz0ZcCAAaU6c52omJycnCr0OvbV1dmzZ3n99dfp2bMnDg4OpKSkEBYWRnh4OA4ODgUulSy0VcjM9fXXX2NtbY2LiwspKSm5VlEqiqSkJCZNmsT169d5+umncXJy4uTJk3z99ddcuXKFRYsWlUHk1c/48ePZt28fISEhJCUlYWlpSe/evZk2bVqFucsWQuhXs2bNGDBgAGfOnOHQoUNkZmZSv359pkyZwksvvVSmS4xWNRWyTfvWrVuaweiTJ0/m1KlTxb7T/v777/nll1+YM2eOVpXoZ599xooVK1ixYgXdunUr1biFEEKIslQh27RLY/aYzZs3Y2ZmlmvieHXX+s2bN5f4OYQQQojyVCGTdkk9ePCAqKgoXFxccq20ZG9vT/369QkLC9NTdEIIIYRuKmSbdkmpZ/jJb0k0e3t7IiIidLr2mTNnUBQl3/G1Qgghqr7MzExUKhUdO3Ys1+etkklbPW2keiac/zIxMclzKtCiUBQFRVHKfGUpIYSoqhIzE4nPKL0ljcuTChWmmGJrbouxYfnfvFXJpK2uEs8vsaanp+eqNi8qY2NjMjIyaNq0aa7pJ0XeUlNTuXHjhpRZMUm5FZ+UmW7Ku9y+O/4d7x18vLhHD4ceNLIu2sx++maUaYRDlAM1UmvQ16MvNaxrlH8M5f6M5UBdLZ7fRPgxMTH5Vp0Xlbm5OTVqlP8frDKTMtONlFvxSZnpprzK7cla0Nd7vM7Y1mPL/DlL6tatWwQEBJCUmoSZmRlWZqUz7WpxVcmOaHZ2djRo0IDw8PBc1eDR0dHcvXu32HMJCyGEqH4UReHEiROsWLGCpKQk6taty/Tp0/XWr6nSJ+3ExESuXbuWawKWUaNGkZaWxrp167S2//HHH8Dj1aOEEEKI/GRlZbFlyxZ27NhBTk4Obdq04bnnntPrZDAVsnp806ZN3LlzB4CoqCgUReGXX37R7J8xY4bm37t372bu3Lm5JvufPn06u3btYtGiRURFReHk5MSpU6cIDg5mxIgRdO/evfxekBBCiErn4cOHnD9/HpVKpVlas6TLDZdUhUzagYGBHD9+XGvbkyv2PJm086NeCP67775j586drF+/HgcHB954441irV0qhBCieqpXrx6jR4+mRo0aNG/eXN/hABU0aa9atarIx3p5eeHl5ZXnvlq1avHJJ5/wySeflFZoQgghqihFUTh27BiNGjXCwcEBAFdXVz1Hpa1CJm0hhCgLD1MesjViK5subeJW/C19h1Nh5OTkkJaWhtlxMwwMyr6rU0xy7iV99S0zM5OtW7fyzz//YG1tzcsvv6zz0OCyJElbCFGl3U28S/ClYILCgzhw4wDZSra+Q6q4Esr/Kc2N9D+e/tGjR2zYsIGYmBhUKhU9evSoMMsK/5ckbSFElXM97jo7z+4k6FIQR24fQSH3YoZGBkYYqCr9AJpSoyhKuXey6t24N+7N3cv1Of/r2rVrbNy4kbS0NCwsLBg7dixNmzbVa0wFkaQthKj0FEUh/EE468PWs+GfDUQk5L22QDPbZni7eOPl4kXXhl0laf9/KSkphIeH4+LiUm0mpVEUhb/++ou9e/cC4ODgwLhx47C2ttZzZAWTpC2EqJQUReHU3VMEhQcRFB7E5YeX8zyuTZ02mkTdrl47vQ/ZERVHZGQkAB07dmTYsGEYGVX8lFjxIxRCiP8vOyebv2///ThRXwrKtzOZm70bY9uMxdPZEyc7p3KOUlQGKpWKMWPGEBERQdu2bSvNjzlJ2kKICi0jO4MDNw4QeDGQTZc3cS/5Xq5jVKjo3aQ3I1qMoLWqNf079a821byi6CIiIrhy5QrDhg1DpVJhZmZW6aa0lqQthKhwUjJTCL0WSlB4EFsjthKXFpfrGCMDI9ybuePt4s0op1HUs6ynaZsV4kmKonDw4EEOHjwIQJMmTSrc+OuikqQthKgQEtIT2B6xnaBLQey4soOUzJRcx5gbmTOk5RC8XLwY4TgCWzPb8g9UVCppaWkEBwcTEfG4c2Lnzp1xcXHRc1S6k6QthNCbBykP2HxpM0GXgtjz7x4ysjNyHWNtas0IxxF4OXsxpOUQLEws9BCpqIzu37/Phg0bePjwIYaGhowYMYIOHTroO6wSkaQthChXkQmRbLq0iaDwIA7ePEiOkpPrGLsadoxxGoOXixcDmg3A1KhiTnQhKq7Lly8TFBRERkYG1tbW+Pr60qBBA32HVWKStIUQZe5q7FXN0KxjUcfyPMbBygEvFy+8XLzo1bgXRgby9SR0Z2JiQmZmJk2bNmXs2LFYWFSNGhr5VAghSp2iKJy/d14zNOtczLk8j2tRs4VmDHVnh84y2YkokSdndWvWrBlTpkyhcePG5TKfenmRpC2EKBWKonDizgnNHfWV2Ct5Hte2bltNonat61ppxseKii0mJoZNmzbh7e2NnZ0dQIWejlRXkrSFEDrLzsnm8K3DBIYHEnwpmMiEyDyP6+rQFS8XLzydPWlVu1U5RymquvPnz7NlyxYyMzPZtWsXEydO1HdIZUaSthCiWNKz0tl3fR9B4UFsvryZ+yn3cx1joDKgT5M+eDl74eniSUPrhnqIVFR1OTk57NmzhyNHjgDQvHlzPD099RxV2ZKkLYQoVHJGMruu7dJMdpKQnnsNR2MDYwa2GIiXsxejnEZRx6KOHiIV1UVKSgobN27k+vXrAPTs2ZMBAwZUqfbrvEjSFkLkKS4tjm0R2wgKD2Ln1Z2kZqXmOqaGcQ2GthyKl4sXw1sNx8bMRg+Riurm0aNHrFixgvj4eIyNjRk9ejRt2rTRd1jlQpK2EELjfvL9x2OoLwWx99+9ZOZk5jrGxtSGkU4j8XL2YnDLwdQwljm+RfmytrbGxsYGQ0NDfH19qVu3rr5DKjeStIUQXIu9xhd/fcHys8vzTNR1LepqJjvp36w/JoYmeohSVGfZ2dmoVCoMDAwwNDRk3LhxGBoaYmZmpu/QypUkbSGqsYv3L/L54c9Z+8/aXDOTNbJupJnspGejnhgaGOopSlHdJSUlsXHjRurXr8/gwYMBqsxkKcUlSVuIauj03dMsOLSA4PBgFBTNdmtTa17o9AK+rr641XeTMdRC7yIjI/H39ycxMZHo6Gh69OiBlZWVvsPSG0naQlQjf936iwWHFhByNURre23z2rzW7TVmdpkpK2eJCuP06dPs2LGD7Oxs7Ozs8PX1rdYJGyRpC1HlKYrC3ut7WXBoAQduHNDaV9+yPm/1eIsX3F7A0sRSPwEK8R9ZWVmEhIRw+vRpAJydnRkzZgymprJwjCRtIaooRVHYFrGN+YfmczzquNa+JjZNmN1zNs92fBYzo+rVkUdUbIqisH79eq5duwbAgAED6NWrlzTV/H+StIWoYrJzstl4cSOfHf4s10IdjrUdmdtrLhPbTsTY0FhPEQqRP5VKRadOnYiKisLLy4tWrWTa2ydJ0haiisjMzmTNP2v4/PDnRDyM0NrXrl473uv9Ht4u3tILXFQ4iqKQlJSkaa9u3bo1zZs3r3bDuYpCkrYQlVxaVhrLzizjy7++5Gb8Ta19XR268l7v9xjhOEKqF0WFlJWVxfbt27ly5QovvPAC1tbWAJKw8yFJW4hykpieSMjVEIIvBXP5weVCj8/JySEtLQ2z42YFzqccmRCZa9GOfk37Ma/3PAY0GyDJWlRY8fHx+Pv7c+fOHVQqFTdv3qRt27b6DqtCk6QtRBmKTY1l6+WtBIYHEnotlPTs9OJfJPfaHPka2nIo7/V+j56Nexb/eYQoR9evX2fjxo2kpKRgbm7O2LFjad68ub7DqvAkaQtRyu4m3tXM373/+n6ylexcxxiqDIvUtqwoSqF3ysYGxgxpOYR3e79Lp/qddI5biPKgKApHjx5l9+7dKIqCvb09vr6+2Nra6ju0SkGSthCl4Pqj6wRfCiYwPJAjt49ozTKmVt+yPp7Onni5eNGnSZ9Ce2+npKQQHh6Oi4sLNWrIohyiajh58iShoaEAtG/fnuHDh2NsLCMZikqSthA6Cr8fTmB4IEHhQZyJPpPnMc1sm+Hl4oW3izddG3bFQFW11/oVojDt27fnzJkztG/fni5dukifi2KSpC1EESmKwum7pwkKDyLoUhCXHlzK87jWdVrj7eKNl4sX7eu1ly8lUe3dvXsXe3t7VCoVJiYmPP/88wV2rhT5k6QtRAGyc7I5EnnkcaIOD8o1pErtqQZP4eXshaeLJ852zuUcpRAVk6Io/PXXX+zbt4/+/fvTu3dvAEnYJSBJW4j/yMzOZP+N/QSFB7Hp0iZikmNyHaNCRa/GvfBy8cLT2ZMmtk30EKkQFVd6ejpbtmzh4sWLAMTFxRWpY6UomCRtIYDUzFRCr4USdCmILZe3EJcWl+sYIwMj3Ju54+XixWin0dSzrFf+gQpRCTx8+JANGzZw//59DAwMGDZsGG5ubvoOq0qQpC2qpKiEKAIuBpCamVrgcQoKZ6PPsuPKDpIzk3PtNzMyY0jLIXg5ezHCcQQ1zWuWVchCVAkREREEBQWRnp6OpaUl48aNo1GjRvoOq8qQpC2qnBNRJxi8ejCP0h7pdL6ViRUjHEfg7eLNkJZDsDCxKOUIhaiaEhMT8ff3Jzs7m0aNGuHj41Pt178ubZK0RZVy+NZhhq0ZRmJGYrHOq21emzHOY/By8cK9mTumRrJurxDFZWVlxZAhQ7h37x6DBw/G0FAWpyltkrRFlbHv+j5GrhtJSmYK8Hj+7de7vV7oebXNa9O1YVeMDOTjIERx3b9/H0VRqFu3LgBPPfWUniOq2uRbSlQJIVdC8PL3Ii0rDYDBLQYT5BtEDWOZSUyIshIeHs6mTZuwsLBg+vTpmJub6zukKk+Stqj0gsOD8d3oS2ZOJgCjnUazYewGqeIWoozk5OSwf/9+Dh8+DECDBg3IycnRc1TVgyRtUamtP7+eSUGTNItyjGszjtWeqwud11sIoZvU1FQCAwO5du0aAN26dWPgwIEyYUo5kaQtKq3lZ5czbfM0zeIcU9pPYemopdI2LUQZiYmJYcOGDTx69AgjIyNGjRol61+XM/l2E5XSryd+ZcaOGZrHL7q9yC/Df5EFOYQoQ/v37+fRo0fY2tri6+uLvb29vkOqdiRpi0rn2yPf8kboG5rHr3Z9lW8HfyvTIwpRxkaNGsXu3bsZOHCgLBerJ3JbIiqVBX8u0ErYc3rOkYQtRBlJTk7m2LFjmsc1atRg9OjRkrD1qMLeaYeGhuLn50dERATGxsa4ubnx2muv4exctBWUzpw5w2+//UZ4eDiPHj3Czs4ONzc3pk+fjqOjYxlHL0qboii8v/99FhxaoNn2Sb9PmNdnniRsIcrAnTt38Pf3Jz4+HmNjYzp16qTvkAQVNGkHBAQwb948HB0deeutt8jIyGD16tVMmDCBdevWFZq49+7dy8yZM2ncuDFPP/00tWrV4vr16/j7+xMaGsqGDRuKnPyF/imKwluhb/HN0W802xYNXMRbPd7SY1RCVF1nz55l27ZtZGdnU6tWLRo2bKjvkMT/V+GSdkJCAgsXLsTe3p5169ZhaWkJwLBhwxg2bBjz589n9erVBV7Dz88PIyMj1q9fT82a/1vgwc3NjRkzZhAUFMS7775bpq9DlI4cJYeZO2by68lfNdt+HPojM7vM1GNUQlRN2dnZ7NixgxMnTgDQqlUrvLy8MDMz03NkQq3CtWnv2bOHpKQkfHx8NAkbwN7enqFDh3LixAkiIyMLvEZiYiJmZmbY2NhobVf3dJRZeyqH7JxsntvynCZhq1DhN9JPErYQZSAtLQ1/f39Nwu7Tpw8TJkyQhF3BVLikHRYWBkDHjh1z7VNvO3fuXIHX6NmzJwkJCcyePZvw8HBiYmI4cuQI77//PvXq1ePpp58u/cBFqcrMzmRS8CSWn10OgKHKkFWeq3iu03P6DUyIKiohIYHIyEhMTEwYP348/fv3l/4iFVCFqx6PiYkByHP8n3qb+pj8vPbaayQkJLB161a2bNmi2d6pUycCAwOpU6dOieNMTS14nWbxP+qyKmqZpWel88y2Z9hy5fHfzsjAiBUjVjCm5RhSUlLKLM6KprjlJqTMdJWamkrdunXp168fLVq0oFatWtXqs6YLRVH08qOmwiVt9YfNxMQk1z5TU1OtY/JjbGxMkyZNaN++PcOGDcPe3p5Lly6xbNkyXnzxRZYtW4atrW2J4rxx40aJzq+OilJmadlpvHPyHf6+/zcAJgYmfOn2JU45ToSHh5dxhBWTvNeKT8qscNnZ2Vy6dIlmzZpphnBZWloSExNT6I2ReCyvPFXWKlzSVrc3Z2Rk5NqXlpamdUx+Zs+ezcGDBwkJCdHcVbu7u9OxY0eeffZZfv31V+bOnVuiOJs2bSpt40WUmprKjRs3Ci2z5IxkfIJ9NAnb3Mgcf09/BjQdUF6hVihFLTfxP1JmRZOYmMjmzZu5e/cuKSkpeHt7c+vWLSm3Yrhy5YpenrfCJe169eoBEB0dTYsWLbT2qX/9qY/Jy927d9m2bRv9+/fPVQ3eo0cPrK2ttSYL0JW5ublMMFBMBZVZQnoCnkGe/HX7LwAsTSzZ/vR2+jTpU54hVkjyXis+KbP83bp1C39/f5KTkzEzM8Pd3R0LCwtAyq049NXeX+E6orVr1w54PDnKf6m3FTRBfXR0NPC46ue/FEUhOzs7z31Cf2JTY/FY6aFJ2LZmtuyZvEcSthClSFEUjh8/zooVK0hOTqZu3bpMnz6dVq1a6Ts0UQwVLml7eHhgYWFBQEAASUlJmu3R0dGEhITg5uZGo0aNgMdVPNeuXSM2NlZzXLNmzTA0NOTkyZPcvn1b69ohISEkJydrfhgI/buffJ8BKwZw4s7jYSa1zWuzb8o+ujbsqufIhKg6srKy2LJlCyEhIeTk5ODq6spzzz1HrVq19B2aKKYSVY8/fPiQXbt28e+//5KamsqCBY+nmIyNjSUyMhJHR8dij/GzsbFh9uzZfPDBB0yYMAFfX18yMzNZtWoViqIwb948zbG7d+9m7ty5zJw5k1mzZgFga2vLM888w9KlSxk3bhzjx4/H3t6e8PBwNm7ciK2tLS+++GJJXrYoJXcT7+K+0p3wB487mNWzqMeeKXtwreuq58iEqHru3buHSqXCw8OD7t27y3CuSkrnpB0QEMCCBQtIT0/XdH1XJ+0HDx7g6+vLJ598go+PT7Gv7evri42NDUuXLmXRokWaucdff/31Ik0/+vbbb9O8eXM2btzI8uXLycjIoFatWgwfPpyZM2dq7tSF/tyKv4X7Sneuxl4FoKF1Q/ZO2YtjbZkXXojSZmRkxLhx44iNjaVZs2b6DkeUgE5J+6+//uKDDz7AycmJWbNmcfjwYdavX6/Z7+joSMuWLdm7d69OSRtgyJAhDBkypMBjvLy88PLyyrVdpVIxduxYxo4dq9Nzi7J1LfYa7ivduRl/E4Cmtk3ZN2UfzWrKl4kQpUFRFI4ePUp6ejr9+vUDHtdi/neWSFH56JS0f//9d+rUqcPq1auxtLTMc/ysk5MTZ8+eLWl8ooq59OAS7ivduZN4B4BWtVqxd8peGtlI7YcQpSEzM5MtW7Zw/vx54PH84Q4ODnqOSpQWnZL2+fPnGTZsmNbc4P9lb2/PgwcPdA5MVD3n759nZMBI7iXfA6BNnTbsmbIHe8vcs98JIYrv0aNHbNiwgZiYGAwMDBg0aBANGjTQd1iiFOmUtDMzMzXj+vKTkJCAgUGF65wu9CQ8LpxX97xKbNrjnv4d7Duwe/Ju7GrY6TkyIaqGa9eusXHjRtLS0rCwsMDHx4cmTZroOyxRynRK2g4ODpqql/ycO3dOOjwIAI5FHeOloy+RnJUMQBeHLuycuJOa5jULOVMIURRHjhxh9+7dKIqCg4MD48aNw9raWt9hiTKg062wu7s7J0+eJDQ0NM/9gYGBXL58mcGDB5coOFH5HbhxgJEBIzUJu3fj3uyevFsSthClqEaNGiiKQseOHXnmmWckYVdhOt1pP//882zfvp3XXnuNwYMHk5CQAMDq1as5efIku3fvpkmTJkyaNKlUgxWVy66ruxizYQxpWY/njO/XuB/bJm7DwqTgphUhROGeXGWqffv21KpVS4azVgM63Wnb2NiwevVq3NzcCAkJ4a+//kJRFObPn8/OnTvp2LEjK1askDlsq7Etl7cwav0oTcLuVbcXgd6BkrCFKAUREREsXrxYa9ZISdjVg86TqzRo0IBVq1Zx6dIlzp49S1xcHFZWVrRv3x5XV5nRqjoLuBDA00FPk5WTBcAYxzHMbjkbM6PizY4nhNCmKAp//vknBw4cAODw4cOFzmchqpYSr/Ll7OxcpFnKRPWwKmwVz2x+hhwlB4Cn2z7Nr4N+5cpl/SxjJ0RVkZaWxqZNm7h8+TIAnTt3ZuDAgXqOSpQ3nTuirVy5ssBj1qxZg7u7u05BicppyaklTN00VZOwn+v4HCvHrMTIoMKtACtEpXL//n38/Py4fPkyhoaGjB49mmHDhmFoaKjv0EQ50+nbNCoqStP5LD8JCQncuXNHp6BE5fPDsR94deermsf/1/n/+GHoDxioZKy+ECVx69Yt1qxZQ0ZGBtbW1vj6+sqEKdVYmd0CpaSkYGxsXFaXFxXIF4e/YM7eOZrHb3V/iy8HfimrCAlRCurWrYuVlRVWVlaMHTu20ImtRNVW5KT937vmxMTEPO+ks7OziY6OJjQ0VHozVnGKovDxwY/5+ODHmm0f9PmAj/p9JAlbiBJIT0/HxMQElUqFmZkZU6ZMwdLSUmaZFEVP2gMGDND6Il65cmWB7dqKojBnzpx894vKSVEUwmLCCAoPIig8iAv3L2j2fe7+OXN6yd9ciJKIiYlhw4YNdO3ala5duwLIZClCo8hJe8yYMahUKhRFYdOmTTg5OeHi4pLrOAMDA2xtbenevTu9evUq1WCFfuQoORyLPPY4UV8K4t9H/+Y65rvB3/Fqt1fzOFsIUVTnz59ny5YtZGZmcvz4cdzc3DAyko6c4n+K/G5YuHCh5t+bNm3Cw8ODmTNnlklQQv+ycrL48+afBF4MJPhSMHeT7uZ5XI9GPXij2xt4t/Yu5wiFqDpycnLYs2cPR44cAaBFixZ4e3tLwha56PSOuHTpUmnHISqAtKw09vy7h6DwIDZf3kxsamyuYwxVhvRr2g9vF29GO4+mgZX0YhWiJFJSUti4cSPXr18HoGfPngwYMEDar0We5GdcNZeUkUTIlRACwwPZfmU7SRlJuY4xNTRlUItBeLl4MdJxJLVr1NZDpEJUPVlZWfj5+fHo0SOMjY0ZM2YMrVu31ndYogIrUdI+d+4chw8fJiYmhoyMjFz7VSoVn332WUmeQpSB2NRYtl7eStClIHZd3UV6dnquYyyMLRjuOBwvZy+GtRqGlamVHiIVomozMjKic+fOnDx5El9fX+rWravvkEQFp1PSzsnJ4Z133mH79u2alWYURdHsVz+WpF1xRCdFs+nSJoLCg9h/Y79mXvAn1TSryWjn0Xg5ezGwxUCZK1yIMpCdnU1ycrKmR3i3bt1wc3PDxMREz5GJykCnpL1q1Sq2bdvGyJEjmTp1KmPHjmXq1KkMHTqU48ePs2TJEvr27csbb7xR2vGKYrgRd4Pg8GACwwP5+/bfKCi5jrG3tMfT2RMvFy/6NumLsaFMiCNEWUlKSiIgIICUlBSef/55TE1NUalUkrBFkemUtDdv3kyTJk1YtGiRZpuVlRUdOnSgQ4cO9OrVi3HjxtGjRw+8vaVXcXkKvx+uGZp1+u7pPI9patsUL2cvvFt7061hN5lqVIhyEBkZib+/P4mJiZiamnLv3j2ZgEoUm05J+/r164wePVprW3Z2tubfrVu3pn///qxdu1aSdglFJUTx7r53+Sfmn0KPjU+Pz3MMNUDrOq3xcvbCy8WLDvYdZMYyIcrRqVOnCAkJITs7Gzs7O3x9fbGzs9N3WKIS0rkj2pMz9JibmxMfH6+1v0mTJhw+fFj3yAQ3424yYOWAfBNxYdzqu+Hl8jhRO9vJ8qlClLesrCxCQkI4ffpxrZeLiwujR4/G1NRUz5GJykqnpF23bl1iYmI0jxs1asSFCxe0jrl58yY1atQoWXTV2NXYqwxYMYDbCbcBMFAZFLrEpYHKgKcaPIW3izeezp40sW1SHqEKIfIRGhqqSdgDBgygV69eUsslSkSnpN2uXTutJN2nTx+WLl3Kzz//zKBBgzh27Bh79+6lX79+pRVntXLx/kU8VnpoZiFzqu3E3il7cbB20HNkQoji6NOnDzdv3mTgwIG0bNlS3+GIKkCnHkiDBw8mOzub27cf3wU+//zzNGjQgB9//JFRo0Yxf/58rKysePPNN0s12OogLDqMfsv7aRJ227ptOfjMQUnYQlQCiqJw69YtzWNLS0teeuklSdii1Oh0p+3h4YGHh4fmsa2tLZs2bcLf359bt27h4ODAmDFjZKKAYjoRdYLBqwfzKO0RAJ3qdyJ0UqjMQCZEJZCZmcn27dsJCwvDy8uLtm3bAkh1uChVpTaNqZWVFc8991xpXa7a+evWXwxdM5TEjEQAujfszo6JO7A1s9VvYEKIQsXFxeHv78/du3dRqVSkpKToOyRRRZXZAN3Dhw/j4+NTVpevUvZd38eg1YM0Cbtvk77smrRLErYQlcD169f5/fffuXv3Lubm5kyaNEmzDrYQpU2nO+24uDiMjIywtLTMte/MmTN88803nDx5ssTBVQchV0Lw8vciLSsNgEEtBhHsG0wNY+l5L0RFpigKR48eZffu3SiKgr29Pb6+vtja2uo7NFGFFStp7969my+//JLIyEgAWrVqxccff0zHjh2JjY3lo48+0ryBXVxceOWVV8ok6Kpi86XN+AT4kJmTCcBIx5H4+/jLnN9CVAJRUVGEhoYC0L59e4YPH46xsUwDLMpWkZP26dOnefXVV8nJydFsi4iI4IUXXmDVqlXMmDGDO3fu0KpVK2bNmsWgQYPKJOCqIjUzlWc3P6tJ2D6tfVjttRoTQ5mDWIjKoGHDhvTp0wcLCws6d+4sHc5EuShy0l6+fDk5OTm88cYb+Pj4oCgKAQEBfPvtt0ydOpWUlBTef/99JkyYIIu3F0FUYpSml3ivxr1Y67220MlThBD6de3aNerUqaOZEbJ///56jkhUN0XOrmfPnqV79+688MIL1KxZk1q1avHiiy/SrVs3EhISeP/995k4caIkbB00sWkiCVuICkxRFA4dOsTq1avx9/cnKyv30rZClIciZ9jY2FjatGmTa7t625AhQ0ovKiGEqCDS09MJCAhg3759ANSrV0/PEYnqrMi3d1lZWZiZ5e4gZW5uDmgvICKEEFXBw4cP2bBhA/fv38fAwIBhw4bh5uam77BENSZ1skIIkYeIiAiCgoJIT0/H0tKScePGyfrXQu+KlbSDg4M5fvy41raoqCgApkyZkut4lUrFihUrShCeEEKUv5ycHPbv3096ejqNGjXCx8cHKysrfYclRPGSdlRUlCZJ/9d/kznInLtCiMrJwMAAHx8fTp48ibu7O4aGhvoOSQigGEl75cqVZRmHEELo1f3797l165amzbpWrVoy34SocIqctLt06VKWcVQ7cWlx+g5BCPH/hYeHs2nTJjIyMrC1taVFixb6DkmIPElHND24GnsVb39vzePGNo31GI0Q1Ze67frw4cMANG3aFHt7ez1HJUT+JGmXs/D74bivdOdu0l0AHGs78mrXV/UclRDVT2pqKoGBgVy7dg2Abt26MXDgQJkgSlRokrTLUVh0GANXDeR+yn0A2tRpw54pe6hnKZM1CFGeYmJi2LBhA48ePcLIyIhRo0bRtm1bfYclRKEkaZeTk3dOMmjVIM184x3tOxI6ORS7GnZ6jkyI6icyMpJHjx5ha2uLr6+vVImLSkOSdjn469ZfDFs7jIT0BAC6NexGyMQQbM1s9RuYENWUm5sb2dnZtG3bVjOroxCVgTTelLH91/czePVgTcLu06QPoZNCJWELUY6Sk5PZvHkzqampmm1dunSRhC0qnQp7px0aGoqfnx8REREYGxvj5ubGa6+9hrOzc5GvceTIEf744w/CwsJISUnBzs6Odu3asWDBAiwtLcsw+sd2Xt2J5wZP0rLSABjYfCCbxm+ihnGNMn9uIcRjd+7cwd/fn/j4eNLT0xk3bpy+QxJCZyVO2snJydy8eZOUlBSeeuqp0oiJgIAA5s2bh6OjI2+99RYZGRmsXr2aCRMmsG7duiIlbj8/PxYtWkSXLl146aWXsLS05P79+5w5c4bU1NQyT9qbLm1iXMA4MnMyARjhOIIAnwDMjHIvuiKEKBtnz55l27ZtZGdnU6tWLfr166fvkIQoEZ2TdnR0NPPnz2f//v3k5OSgUqm4ePEiACdPnuSDDz7gww8/pGvXrsW6bkJCAgsXLsTe3p5169ZpkuuwYcMYNmwY8+fPZ/Xq1QVe4/jx43z11VdMnz6dt956S7cXWAIhV0IY6z+WbCUbAG8Xb9Z6r8XE0KTcYxGiOsrOzmbHjh2cOHECAEdHRzw9PfNcqVCIykSnNu179+7h4+PDvn376N+/Px06dEBRFM3+9u3b8/DhQ3bs2FHsa+/Zs4ekpCR8fHy07obt7e0ZOnQoJ06cIDIyssBr/Prrr9ja2vLKK68Aj2sDynPR+s8Pf65J2BPbTmT92PWSsIUoJ+np6fj7+2sSdt++fRk/frwkbFEl6HSn/dNPPxEbG8sff/xB165d+emnnzh79qxmv7GxMU899RSnT58u9rXDwsIA6NixY659HTt2ZOPGjZw7d46GDRvmeX5qairHjx+nd+/ebN++nZ9++onIyEiMjIzo0qULs2fPLla7eH6e7NDyXw9SHgBgbmTOr4N+JSMtgwwySvyclZW6rAoqM5GblFvxpaamolKpiI+Px8TEhOHDh9OyZUspw0LIe634FEXRy6JYOiXtP//8kwEDBhRY9V2/fn1OnjxZ7GvHxMQA5DluUr1NfUxebt68SVZWFufOnePw4cM899xzuLq6cvHiRX7//XcmTJhAYGAgzZs3L3ZsT7px40a++9LT0x//Q4GIyxElep6qpKAyE/mTciseExMTOnbsiIGBAZmZmYSHh+s7pEpD3mvFY2JS/jWoOiXtBw8e0KRJkwKPMTY21ulXm/qcvArD1NRU65i8JCUlAfDw4UM++eQTfH19ARg4cCANGjRg3rx5/Pzzz3z99dfFju1JTZs2zXe4iOlRU0h8vLyfi4tLiZ6nKkhNTeXGjRsFlpnITcqtaLKysti7dy/29vY4Ojpy48YN2rVrJ2VWDPJeK74rV67o5Xl1Stq2trZER0cXeMz169exsyv+bF/qN0xGRu7q5LS0NK1j8qJutzIwMMDT01Nr35gxY/jwww85evRosePKK84aNfIeumWg+l9XgfyOqY4KKjORPym3/CUkJBAQEEBkZCQXL17UrM4lZaYbKbei00fVOOjYEa1Tp07s27ePhw8f5rn/xo0bHD58uNg9xwHq1Xs8D3dePwrU1eLqY/JSv359AKytrXPdrRsbG1OzZk3i4+OLHZcQomK5desWS5YsITIyEjMzM3x9fctl/gUh9EmnpP3cc8+Rnp7OpEmT+PPPPzXV1SkpKRw8eJCXXnoJlUrFtGnTin3tdu3aAXDmzJlc+9TbCprYv3bt2jRs2JD4+HhSUlK09qWnpxMbG0vt2rWLHZcQomJQFIXjx4+zYsUKkpOTqVevHi+88AItW7bUd2hClDmdknb79u355JNPuH37Ni+++CLLli0DHs/n+9JLLxEZGcmCBQto1apVsa/t4eGBhYUFAQEBmvZpeHznHRISgpubG40aNQIgMTGRa9euERsbq3UNT09PFEVhzZo1WtvXrFlDTk6OTLAgRCWlKApbtmwhJCSEnJwcXF1dmTZtGjVr1tR3aEKUC50nV/H29sbNzY21a9cSFhZGXFwclpaWdOjQgYkTJ+rcO9vGxobZs2fzwQcfMGHCBHx9fcnMzGTVqlUoisK8efM0x+7evZu5c+cyc+ZMZs2apdk+bdo0QkND+frrr7lx44am9/jGjRupX7++1rFCiMpDpVJRs2ZNVCoVAwcOpFu3bnprWxRCH0o0jWnTpk159913SysWDV9fX2xsbFi6dCmLFi3SzD3++uuvF2mMdY0aNVi9ejU//fQToaGhbN68GVtbW7y9vXnllVd06iAnhNCfnJwcDAweVwz27t2bVq1aafqvCFGd6JS0r169WubtR0OGDGHIkCEFHuPl5YWXl1ee+6ytrXn33XfL5EeFEKJ8KIrC0aNH+eeff3j22WcxNjZGpVJJwhbVlk5t2iNGjMDHx4c1a9YQFxdXyiEJIcTjYZ9BQUGEhoZy9+5dzp07p++QhNA7ne60u3XrxvHjxzl//jwLFy6kf//+eHp60qdPHwwNDUs7RiFENRMbG4u/vz8xMTEYGBgwePBgOnXqpO+whNA7nZL28uXLiYmJYfPmzWzatInQ0FB2795NzZo1GTlyJJ6enqUyv7cQovq5evUqgYGBpKWlYWFhgY+PT6EzMApRXehUPQ5oxkbu2LGDgIAAJkyYQE5ODitWrMDT05PRo0ezfPnyUgxVCFHVhYWFsWbNGtLS0nBwcOCFF16QhC3EE3RO2k9q27YtH3zwAYcPH+ann37C3d2da9eu8eWXX5bG5YUQ1UTTpk2pUaMGnTp14plnnsHa2lrfIQlRoZRoyNd/paWlERsbS2xsLFlZWTJ+UghRqNTUVM16AjY2Nrz00ktYWVnpOSohKqYSJ21FUTh06BCbNm1i7969moU+unTpkmvBDiGEeNLly5cJDg5mzJgxmn4wkrCFyJ/OSfvKlSsEBwezdetWHjx4gKIoNG7cmNGjR+Pp6UmDBg1KM04hRBWiKAoHDx7k4MGDAJw+fRonJyepnROiEDolbS8vL8LDw1EUBUtLS8aOHcuYMWNwc3Mr7fiEEFVMWloawcHBREREANC5c2cGDx4sCVuIItApaYeHh9OjRw88PT0ZOHAgpqampR2XEKIKun//PuvXryc2NhZDQ0NGjBhBhw4d9B2WEJWGTkn7wIEDBa5pLYQQ/5WQkICfnx8ZGRlYW1vj6+srzWhCFJNOSVsSthCiuKytrenYsSMxMTGMHTsWCwsLfYckRKVTpKR94sQJANq1a4epqanmcVF07txZt8iEEJVeamoqOTk5mgQ9cOBAVCqVZsUuIUTxFClpT548GZVKxY4dO2jWrJnmcVGEh4eXKEAhROUUHR3Nhg0bsLGxYfLkyRgaGsraBEKUUJGS9v/93/9pFp9/8rEQQuTln3/+YcuWLWRlZQGQmJiIra2tfoMSogooUtKeNWtWgY+FEAIgJyeH3bt3c/ToUQBatGiBt7e3ZsYzIUTJ6NQR7c6dO1hbW2NpaZnvMUlJSSQkJEjvUCGqieTkZAIDA7l+/ToAvXr1on///tJ+LUQp0unT5O7uzooVKwo8ZtWqVbi7u+sUlBCi8gkODub69esYGxvj4+ODu7u7JGwhSplOnyhFUVAUpbRjEUJUYoMHD6Z+/fo8//zztG7dWt/hCFElldnP4IcPH0o7lhBVWHZ2tqYqHKBOnTpMnz6dunXr6jEqIaq2Irdpb9q0SevxpUuXcm2Dxx/ku3fvsmXLFhwdHUsanxCiAkpKSiIgIIDbt28zZcoUmjZtCiCjSoQoY0VO2nPmzNF8IFUqFXv37mXv3r25jlNXm5ubmzNz5sxSClMIUVFERkbi7+9PYmIipqamZGZm6jskIaqNIiftzz//HHiclN999108PDzy7GhmYGCAra0tHTt2xNrauvQiFULo3alTpwgJCSE7Oxs7Ozt8fX2xs7PTd1hCVBtFTtqenp6afwcHB+Ph4cGYMWPKIiYhRAWTlZVFSEgIp0+fBsDFxYXRo0fLCn9ClDOdxmmvWrWqtOMQQlRgFy9e1CTsAQMG0KtXL2m/FkIPdEraQojqpW3btty+fRsnJydatmyp73CEqLaKlLTd3d1RqVT88ccfNGrUqMiTpqhUKvbs2VOiAIUQ5U9RFMLCwnBxccHU1BSVSsXw4cP1HZYQ1V6RkvZ/J1Mp6sQqMgGLEJVPZmYm27dvJywsjEuXLuHr6ytV4UJUEEVK2vv27SvwsRCiaoiLi8Pf35+7d++iUqlo0qSJvkMSQjxB2rSFEABcv36djRs3kpKSQo0aNRg7dizNmjXTd1hCiCeUatLOysriypUrmJiY0KJFi9K8tBCijCiKwtGjR9m9ezeKolC/fn3GjRsn618LUQHpNPf4jh07ePXVV4mLi9Nsu337NiNGjMDLy4sRI0YwY8YMsrKySitOIUQZSUtL48iRIyiKQvv27Xn22WclYQtRQel0px0QEMCDBw+0PtgLFy7kxo0bdOvWjbi4OPbv309QUBDjxo0rrViFEGXA3NyccePGcefOHTp37iydzoSowHS607569Spt27bVPE5KSuLgwYMMHTqU5cuXExAQQPPmzQkKCiq1QIUQpefq1atcuHBB87hhw4Z06dJFErYQFZxOd9rx8fHUqVNH8/jMmTNkZWVpxnEaGxvTo0cPtm/fXjpRCiFKhaIoHD58mH379mFkZESdOnVkKU0hKhGdkraFhQVJSUmaxydPnkSlUtGpUyfNNlNTU5KTk0seoRCiVKSnp7N582bCw8MBaNeuHbVq1dJzVEKI4tApaTdp0oQ///yT9PR0VCoVISEhODk5aX0B3Llzh9q1a5daoEII3T148IANGzbw4MEDDA0NGTp0KG5ubvoOSwhRTDol7bFjxzJv3jwGDRqEsbExUVFRvPvuu1rHhIWFyRzFQlQAly9fJjg4mPT0dKysrBg3bhwNGzbUd1hCCB3o1BFt7NixPPfcc6SlpZGYmMjEiROZNGmSZv+RI0eIioqia9eupRaoEEI3kZGRpKen07hxY1544QVJ2EJUYjpPrvL222/z9ttv57nPzc2NEydOYG5urnNgQojS0b9/f6ytrenUqROGhob6DkcIUQI63WkXxsTEBCsrK4yMZJZUIcrb/fv3CQoK0kxuZGBgQOfOnSVhC1EFlCirRkVFsXnzZi5evEhiYiJWVla0adOGUaNG4eDgUFoxCiGK6OLFi2zevJmMjAysra3x8PDQd0hCiFKkc9Jet24dn332GVlZWVpLcO7Zs4dffvmF9957j/Hjx5dKkEKIguXk5LB//34OHz4MQNOmTenevbueoxJClDadkvbff//NJ598Qo0aNZg2bRo9evSgTp063L9/n6NHj7Jq1So++eQTmjRpIl8cQpSx1NRUAgMDuXbtGgDdu3fHw8MDA4Myaf0SQuiRTkl76dKlWFhYsHHjRpo2barZ3rx5c7p27YqnpydeXl74+flJ0haiDN27d49169YRFxeHkZERo0aN0ppiWAhRteiUtP/55x+GDh2qlbCf1LhxY4YMGUJoaGhJYhNCFMLY2Ji0tDRq1qyJr68v9erV03dIQogypFPSVn9JFKRWrVqkpaXpFJQQIn+KomgW9qhZsyYTJ06kdu3aMsRSiGpAp0av+vXrc+zYsQKPOXbsGPXr19cpKIDQ0FDGjRtHhw4d6Ny5My+99BKXLl3S6Vpr1qzByckJJycnoqOjdY5JCH1LTk5m1apVXLlyRbOtYcOGkrCFqCZ0StoDBw7k3LlzfPrppyQmJmrtS0xMZP78+Zw7d45BgwbpFFRAQACzZs0iNTWVt956i5dffpmIiAgmTJhQ7MQdHR3N119/TY0aNXSKRYiK4s6dOyxZsoTr16+zbds2srOz9R2SEKKc6VQ9/uKLL7J3717WrFnDpk2bcHFxwc7OjgcPHhAeHk5ycjLNmzfnxRdfLPa1ExISWLhwIfb29qxbtw5LS0sAhg0bxrBhw5g/fz6rV68u8vU++ugjmjZtSosWLdiyZUux4xGiIjh79qwmUdeqVYvx48fLZClCVEM63WlbWVmxYcMGfHx8yM7O5uTJk+zcuZOTJ0+Sk5PDuHHjtBJucezZs4ekpCR8fHy0zre3t2fo0KGcOHGCyMjIIl1rx44dHDx4kI8//li+4ESllJOTw549e9i8eTPZ2dk4Ojoyffp0rfXshRDVh86Tq1hbW/Ppp5/ywQcfcP36dc2MaM2aNcPY2FjngMLCwgDo2LFjrn0dO3Zk48aNnDt3rtBFD+Lj41mwYAGTJk2SITCiUsrMzOTo0aPExsYC0LdvX/r27avphCaEqH6KlbSTkpJYtWqVJrG2b9+eSZMm4ejoWGoBxcTEAI/vrP9LvU19TEEWLlyIkZERr776aqnF9qTU1NR89+UoOZp/p6SklMnzVybqsiqozERuWVlZWFhYkJSUxPDhw2nZsqWUYSHkvaYbKbfie3IUR3kqctJOTEzEx8eHmzdvaqYtPXjwIJs2bSIgIABra+tSCUj9pjExMcm1z9TUVOuY/Bw5coSgoCB+/vlnnaroi+LGjRv57ktPTwceV22Gh4eXyfNXRgWVmfif7OxsTXOOq6srLVu2JDMzU95LxSDvNd1IuRVPXnmqrBU5afv5+XHjxg1atmzJmDFjUBSFzZs3c+3aNX7//XfefPPNUglIPXQlIyMj1z71uO+Chrekpqby/vvv4+7uXqaLJTRt2jTfOEyPmkLi49WVXFxcyiyGyiI1NZUbN24UWGbi8Z313r17SUhIwNvbm/T0dG7cuEGbNm2k3IpI3mu6kXIrvieHXZanIiftffv2Ua9ePQICAjR/1EmTJjFkyBD2799faklbPaNTdHQ0LVq00NqnrhYvaNYnPz8/oqOjWbhwoVaHNXU1dXR0NFlZWYW2iRfG3Nw832FkBqr/9e+ToWb/U1CZVXcJCQn4+/sTFRUFwIMHD6hbty4g5aYLKTPdSLkVnb76lhQ5aUdGRjJ69GitX2Hm5ub079+fzZs3l1pA7dq1Y/369Zw5c4aePXtq7Ttz5gxAgR3LoqKiyMzMZOLEiXnu9/X1BeDChQuy3reoEG7evElAQADJycmYmZnh7e1N06ZNpT+EECKXImet1NRU7Ozscm23s7Mr1elKPTw8WLBgAQEBATzzzDOaNuno6GhCQkJwc3OjUaNGwON29nv37lGzZk1q1aoFwOTJk/OsFl+5ciXHjh1j/vz51KxZU4aACb1TFIUTJ06wa9cucnJyqFu3Lr6+vpr3shBC/FeFu9W0sbFh9uzZfPDBB0yYMAFfX18yMzNZtWoViqIwb948zbG7d+9m7ty5zJw5k1mzZgHQpk0b2rRpk+u6e/bsAaB379559kwXorzt27dPs/61q6srI0eO1EvHFiFE5VGspH3p0iU2bdqktU3do/W/29XGjBlT7KB8fX2xsbFh6dKlLFq0CGNjY9zc3Hj99ddxdnYu9vWEqIjatGnD8ePH6devH926dZPx10KIQqkU9fitQjg7O+f7pVLQeLWqNkzln3/+ISMjAxcXl3w7bLj+4sqF+xewMLYg6d2kco6w4klJSSE8PLzAMqsukpOTsbCw0DxOSUnJt0yk3IpPykw3Um7Fd+7cOVQqVblP3lXkO21PT8+yjEOIKk1RFI4cOcL+/fuZOnWqZvSCfEEKIYqjyEn7888/L8s4hKiyMjIy2Lp1K+fPnwfg4sWLJR5yKISonipcRzQhqpLY2Fg2bNjAvXv3MDAwYPDgwXTu3FnfYQkhKilJ2kKUkatXrxIYGEhaWhoWFhb4+PjQpEkTfYclhKjEJGkLUQZu377NmjVrAHBwcGDcuHGlNj+/EKL6kqQtRBlo2LAhzs7O1KhRg6FDh8rse0KIUiHfJEKUktjYWCwtLTExMUGlUjF27FiZeU8IUaoMCj9ECFGYy5cvs2TJErZu3apZulYSthCitMmdthAloCgKBw8e5ODBg8Dj1boyMzNlOlIhRJmQpC2EjtLS0ggODiYiIgKALl26MGjQILnDFkKUmRIl7b1797J161b+/fdfUlNT2b17NwDXrl1j3759jBo1qsC1r4WorO7fv8/69euJjY3FyMiIESNG0L59e32HJYSo4nRK2oqiMHv2bLZu3QqAmZmZ1vKc1tbWfPvttyiKwgsvvFA6kVYiCkWazl1UUjk5Oaxbt45Hjx5hY2PDuHHjaNCggb7DEkJUAzp1RFu7di1btmzBy8uL48ePM23aNK39derUoVOnTpp2vupkxdkVXHpwCQBzY3M9RyPKgoGBAaNGjaJFixZMnz5dErYQotzodKe9ceNGnJ2dmT9/PiqVKs8Vvpo0aaJZK7i6WHxyMS9vf1nz+OWnXi7gaFGZpKamcu/ePc2MZk2bNqVJkyaynKYQolzpdKd9/fp1unbtWuAXVu3atYmNjdU5sMrmu6PfaSXsWV1m8XG/j/UYkSgt0dHRLFmyhLVr13L//n3NdknYQojyptOdtqGhIenp6QUeExMTU22WHfzs0Ge8t+89zeN3erzDQo+F8qVeBfzzzz9s2bKFrKwsatasSU5Ojr5DEkJUYzol7ZYtW3LixAkURckzMaWnp3P06FFat25d4gArMkVR+GD/B8w/NF+z7aO+H/FB3w8kYVdyOTk57N69m6NHjwLQokULvL29MTeXfgpCCP3RqXp81KhRXLt2jS+++EIz+5NadnY2n3/+Offu3cPT07NUgqyIFEXh7d1vayXshe4L+bDfh5KwK7nk5GRWrVqlSdi9evXi6aefloQthNA7ne60x48fz759+1i+fDk7duzQVIO/8sornD17lnv37uHu7s6oUaNKNdiKQlEU3tjzBkvOLtFs+2HID8zqOkuPUYnScuLECW7cuIGJiQljxozBxcVF3yEJIQRQgjbt3377jV9//ZU1a9Zw7949AEJDQ7G2tmbGjBnMmDGjVAOtSOIy4jQJW4WK30b8xnS36XqOSpSW3r17Ex8fT48ePahTp46+wxFCCA2dZ0QzMjJi1qxZzJw5k+vXrxMXF4eVlRXNmzev8tM4JmclA2CgMmD56OVMbj9ZzxGJksjOzubEiRN07twZQ0NDDA0NGT16tL7DEkKIXEo897hKpaJ58+alEUulYmRgxFqvtfi08dF3KKIEkpKSCAgI4NatW8TFxTFkyBB9hySEEPmSBUN09I37N5KwK7nIyEj8/f1JTEzE1NS0Wv74FEJULjol7SlTphTpOJVKxYoVK3R5igqvmW0zfYcgSuDUqVPs2LGDnJwc7OzsGD9+PLVr19Z3WEIIUSCdkvbx48cL3K9SqfIdwy2EPmVlZbFjxw7OnDkDgIuLC6NHj8bU1FTPkQkhROF0StqXLl3Kc3tiYiL//PMPX331FU2bNmXRokUlCk6I0hYfH8+FCxcAGDBgAL169ZIfl0KISkOnyVXyY2VlRY8ePVi2bBnHjx9n2bJlpXl5IUqsdu3aeHl5MXHiRHr37i0JWwhRqZRq0laztbWlb9++bNy4sSwuL0SRKYrCsWPHuH79umabk5MTLVu21GNUQgihmzJJ2gCWlpbcuXOnrC4vRKEyMzPZvHkzO3fuZOPGjSQnJ+s7JCGEKJEyGfKVlpbGgQMHpDeu0Ju4uDj8/f25e/cuKpWKXr16VZtV54QQVZdOSXvTpk15bs/KyiI6OpqtW7dy69Ytnn/++ZLEJoROrl+/TkBAAKmpqdSoUYOxY8fSrJkM0RNCVH46Je05c+bk2YFHveKXgYEBY8aM4dVXXy1ZdEIUg6IoHDlyhD179qAoCvXr12fcuHHY2trqOzQhhCgVOiXtzz77LM+krVKpsLGxwdXVVRZaEHoRHR2Noii0b9+e4cOHY2xsrO+QhBCi1OiUtL28vEo7DiFKTKVSMXLkSFq2bEnbtm1lOJcQosrRqff4lClT+Pbbb0s7FiGK7erVq2zevFnTNGNsbEy7du0kYQshqiSdknZYWBg5OTmlHYsQRaYoCocOHWLNmjWcPXtWMy2pEEJUZTpVjzdu3Jjo6OjSjkWIIklPT2fTpk2a6XQ7depEu3bt9ByVEEKUPZ3utL29vTl48KBMniLK3YMHD/Dz8+PSpUsYGhoyYsQIRo4ciZGRrDIrhKj6dPqm8/Dw4OjRo0yYMIHnn3+edu3aYWdnl2c7YoMGDUocpBAAV65cITAwkPT0dKysrBg3bhwNGzbUd1hCCFFuipy0N23ahLOzM87Oznh4eGiW3/zss8/yPUelUnHx4sVSCVQIc3NzsrKyaNy4MT4+PlhaWuo7JCGEKFdFTtpz5sxh1qxZODs7M2bMGOmdK8rFk+uyN2zYkClTpuDg4IChoaGeIxNCiPJXrOpx9bCahQsXlkkwQjzp3r17BAUFMWbMGOzt7YHHnSCFEKK6KrNVvoQoiYsXL+Ln50dMTAw7d+7UdzhCCFEhSJdbUaHk5OSwb98+/vrrLwCaNWuGt7e3nqMSQoiKoVhJOzExsdjDvKT3uCiq1NRUAgMDuXbtGgDdu3fHw8MDAwOpEBJCCChm0l65ciUrV64s8vHSe1wUVUJCAn/88QdxcXEYGRkxevRoXF1d9R2WEEJUKMVK2paWllhZWZVVLKIas7S0pHbt2qhUKnx9falXr56+QxJCiAqnWEl76tSpzJw5s6xiEdVMTk4OOTk5GBkZYWBgoGm7Njc313NkQghRMVXYjmihoaH4+fkRERGBsbExbm5uvPbaazg7Oxd4nqIobN26lQMHDnD+/HliYmKwsbGhVatWTJs2jZ49e5bTKxAFSU5OZuPGjdSsWZORI0eiUqkkWQshRCEqZA+fgIAAZs2aRWpqKm+99RYvv/wyERERTJgwQbNIRH4yMjJ4++23uXr1KkOHDmXevHmMHz+eK1euMG3aNJYsWVJOr0Lk586dOyxZsoQbN25w4cIF4uLi9B2SEEJUChXuTjshIYGFCxdib2/PunXrNFNVDhs2jGHDhjF//nxWr16d7/mGhoasWLGCbt26aW338fFhxIgR/PDDD/j6+mJjY1Omr0Pk7ezZs2zbto3s7Gxq166Nr68vNWvW1HdYQghRKVS4O+09e/aQlJSUa25pe3t7hg4dyokTJ4iMjMz3fCMjo1wJG6BOnTp07tyZzMxMrl+/Xiaxi/zl5OSwe/duNm/eTHZ2No6Ojjz//PPUqVNH36EJIUSlUeQ77cKqpUtLWFgYAB07dsy1r2PHjmzcuJFz587ptLpTTEwMALVr1y5ZkKLYTp06pSn/fv360adPH5m/XgghiqnCVY+rv9jVc00/Sb1NfUxxHDx4kHPnzvHUU0/RqFGjkgXJ47bzlJSUEl+nOkhNTaVJkybExcUxfPhwWrRoQWpqqr7DqvDUZSRlVXRSZrqRciu+JxczKk8VLmmr3zQmJia59pmammodU1T//vsv77zzDhYWFsyfP7/kQQLR0dGEZ4eXyrWqqtTUVE2P8Lp169KvXz8yMjIID5dyK44bN27oO4RKR8pMN1JuxZNXniprFS5pq7/kMzIycu1LS0vTOqYobt++zbRp08jIyGDJkiU0a9asVOK0t7fHxdGlVK5V1WRlZbF3714uX77M5MmTMTMz48aNG7Rq1UqGdRVDamoqN27coGnTplJuRSRlphspt+K7cuWKXp63wiVt9UxY0dHRtGjRQmufulq8qLNlRUZGMnXqVOLj4/ntt9/o3LlzqcVpYmJCjRo1Su16VUVCQgL+/v5ERUUBj5fXdHJyAh7/2JIyKz4pt+KTMtONlFvR6atPToXrPd6uXTsAzpw5k2ufelvbtm0LvU5UVBRTpkzh0aNH/P7773Tp0qV0AxW53Lx5kyVLlhAVFYWZmRmTJk3Ks0OhEEII3VS4pO3h4YGFhQUBAQEkJSVptkdHRxMSEoKbm5umI1liYiLXrl0jNjZW6xpRUVFMnjyZuLg4/Pz8eOqpp8r1NVQ3iqJw/PhxVq5cSXJyMvXq1eOFF17IVVMihBCiZCpc9biNjQ2zZ8/mgw8+YMKECfj6+pKZmcmqVatQFIV58+Zpjt29ezdz585l5syZzJo1C4CkpCSmTJlCVFQUEydOJDIyMte47k6dOpVKD3LxWFhYGCEhIcDjWpCRI0dibGys56iEEKLqqXBJG9DMWLZ06VIWLVqkmXv89ddfL3Tu8bi4OE2SXrNmDWvWrMl1zOeffy5JuxS5urpy+vRpXFxc6Natm4y/FkKIMlIhkzbAkCFDGDJkSIHHeHl54eXlpbWtYcOGXL58uSxDEzyeP9ze3h4DAwOMjIx45plnMDCocK0tQghRpci3rCgWRVH4+++/8fPzY9++fZrtkrCFEKLsVdg7bVHxZGRksHXrVs6fPw88Xl5TX7MCCSFEdSRJWxRJbGwsGzZs4N69exgYGDBkyBCeeuopSdhCCFGOJGmLQl29epXAwEDS0tKwsLBg3LhxNG7cWN9hCSFEtSNJWxQoJSWFgIAAMjIyaNiwIT4+PlhbW+s7LCGEqJYkaYsC1ahRgxEjRnDjxg2GDh2KkZG8ZYQQQl/kG1jk8uDBA9LT03FwcAAeT5hSlKljhRBClC0ZpyO0XL58GT8/PzZs2KA1jawQQgj9kzttATwef33gwAH+/PNPoOgrqQkhhCg/krQFaWlpBAcHExERAUDnzp0ZPHgwhoaGeo5MCCHEkyRpV3CZmZlkZ2eX2fUfPnxISEgIcXFx2NjY0K9fP5ydncnMzCQzM7NUniM9PV3zf5k5reik3IpPykw3Um7/Y2xsXKFvWCRpV1AJCQmaDmFlKSUlhTZt2mBgYICFhQWGhoZcv369VJ8jJycHIyMj7ty5U+2/EIpDyq34pMx0I+X2PyqVChsbG+zt7Svk5FGStCughIQEoqKisLS0xM7ODmNj4zJ78+Tk5JCUlKRJ2GUhOzub9PR0TE1NK/Qv2IpGyq34pMx0I+X2mKIoJCcnc//+fczNzbG1tdV3SLlI0q6AHjx4gKWlJQ0bNiz1ZJ2dnU1qaioWFhaaa9eoUaNUnyOv5wQwMzOr1l8IxSXlVnxSZrqRcvsfc3Nz0tPTuXfvHjY2NhXubrt614NUQJmZmaSnp5fJmyUzM5MHDx6QkJBAcnJyqV5bCCGqCmtra7Kzs8u0P5Gu5E67glG/SYyNjUv1uikpKcTHx6MoCoaGhpiampbq9YUQoqpQz/yYlZVV4WaBrFjRCI3SustWFEXrztrU1JSaNWtW+84mQgiRn4pWJf4kSdpVWHZ2No8ePSIjIwMAS0tLrKysKvQbUgghRP4kaVdhWVlZZGRkoFKpsLW1xdzcXN8hCSGEKAGpI63CTE1NsbW1xc7OThK2KJGbN2/i6upKSEiIvkMRJRAZGYmrqytbtmzRdyhCR3KnXYWo269r1Kih6chW1sO5SsOxY8eYMmWK1jYzMzMaN27M4MGDef755zEzM8vz3GvXrvHHH39w9OhRYmJiMDExoVmzZgwePJiJEyfm+/ozMjIIDAxk586dXL58mcTERCwtLXF0dMTd3Z2xY8diaWlZ6q+1svriiy9o2bIlQ4YM0XcoFV5UVBTffPMNf/31FykpKTRt2pRJkyYxbty4Il8jNjYWPz8/9u/fz927d6lRowZNmzZl/PjxjBo1SutYJyenAq/l4+PD/PnzAWjYsCHe3t588803DBo0KN/PVUUzZ84cgoOD891vZGTEhQsXNI83b97Mli1buHr1Ko8ePcLMzIyGDRvi6enJuHHjKnVHXEnaVcST7dfp6enUqVOn0rVdDx48GHd3dwAePXpESEgIP/74I2fPnsXPzy/X8YGBgXz44YeYmZkxZswYHB0dSUtL4++//+arr75i48aN+Pn50ahRI63z7ty5w0svvcTly5fp3Lkzzz77LHZ2diQkJHDy5EkWLVrEn3/+ybJly8rldVd0Fy9eZO/evXzxxReV7j1V3qKjo/H19SUxMZGpU6fSsGFD9u3bx/vvv8+dO3d47bXXCr1GUlISPj4+3L9/Hx8fH5ycnEhKSmLr1q28/fbbXLt2jddff11z/JdffpnndVasWMGFCxcYMGCA1vZnn32W9evXExgYyMSJE0v0esuLr68v3bt3z7X9woULrFixItdrvHjxIlZWVkyYMIHatWuTlpbGiRMnmD9/Pnv27OGPP/6ovJ1xFVEs586dUzYd3KRsvbC1TK6fmpqqXLx4UUlNTS3yOenp6crdu3eVqKgo5c6dO8U6tzxkZWUpSUlJSlZWVp77jx49qjg6Oio///xzrvM8PT0VR0dH5fz587nOcXZ2VgYPHqxER0fnuubOnTsVFxcXZfjw4UpaWppme3p6ujJixAjFxcVF2bo177/hrVu3lO+++664L7PUPVluSUlJeovj3XffVTp06KAkJyeX+rVL+3UV9l4ra2+//bbi6Oio7Nq1S2v7//3f/ykuLi7K9evXC72Gv7+/4ujoqKxatUpre2pqqtKrVy/lqaeeKvQaiYmJSocOHZTevXvnWRbjxo1Thg8frnms73LT1VtvvaU4Ojoqf/75Z5GO/+CDDxRHR0fl+PHjBR5XlO/hsLAw5dy5c8WKtzRU0p8aAv435d6DBw80cwfXqVOn0lR5FcbQ0JCuXbsCcOPGDa19ixYtIicnh2+++SbPZUQHDx7MpEmTuHLlChs3btRsDwgIICIigqlTpzJixIg8n7dRo0a8+uqrRYrx9u3bzJs3j/79++Pq6kqPHj2YNm0af/31l+aYyZMn57oTUHNycmLOnDmax5GRkTg5OfHjjz8SGhrK5MmT6dSpEy+99BL+/v44OTnl2x45YcIE3NzcSEtL02x78OABn376KQMGDNDE99ZbbxEZGVmk15ednc2uXbvo2rVrrqaGnJwcFi9ezOTJk+nVqxeurq707t2b2bNnc+fOnXxf67FjxzSva+TIkZr9t27dYs6cOZpr9enTh48++ojY2Fit68TExPDFF1/g6elJly5dcHV1ZfDgwXz77bdar728paWlsWvXLho2bMigQYO09j3zzDNkZ2ezbdu2Qq+TkJAAQN26dbW2m5mZYWtrW6TP99atW0lJScHb2zvPGc769u3LlStXNCv76eLHH3/EycmJf//9l2+++YZ+/frh6urK0KFD2bp1q87XLaqEhAR27dqFg4MDPXv2LNI5DRs2BCA+Pr4sQytTUj1eSSmKQnx8PCkpKcD/PtCVtsonHzdv3gTQmgP4zp07/PPPP3To0IHWrVvne+6ECRNYsWIFu3bt0lQD7ty5E4Dx48eXOLYLFy7wzDPPkJqaire3N87OziQlJREWFsbff/9d5C+SvOzdu5fly5fj7e3NuHHjUKlUDBs2jAULFrBp06Zc7Zq3bt3i9OnT+Pj4aL7U7969y/jx40lJSWHs2LE0bdqUmJgY1q1bx19//UVgYCANGjQoMI6LFy+SmJhI+/btc+3LzMzEz8+PwYMH079/fywsLLh8+TKBgYEcOXKELVu25Jq7+fz58+zatQsvLy9GjBihmT8gPDycyZMnY2Zmhre3Nw4ODty4cYN169Zx5MgRNm7ciJWVFQCXL19m165dDBo0iIYNG6IoCsePH+e3337jwoULfP/990Uq4//+GCiIubl5oZ05L1++TFpaGh06dMi1r3379hgaGhIWFlboc/Xo0QOVSsVXX32FiYkJzs7OJCYmEhAQwNWrV/n0008LvUZAQAAGBgaMHTs2z/2dOnUCHvcncXR0LPR6BZkzZw4GBgZMmTIFAwMD1q5dy1tvvUWjRo20yiI5ObnICyAZGhpiY2NT4DFbtmwhPT0db2/vfL/3EhMTyczMJDk5mVOnTuHn54eVlZXm9VdGkrQrkYALAXxw4AMS0xOBx3c6CgoGKgO9tjVamVrxaf9PGds67y+IokpLS9N8kT569Iht27axd+9eHBwc6Ny5s+a4y5cvA9C2bdsCr9esWTNq1KihOR4gIiICCwsLmjRpUqJYFUVhzpw5pKSksH79+lyx5OTklOj6V65cITAwkEaNGmnNB+3h4cGOHTuIjo7G3t5ec3xQUBAAXl5emm2ffvopqampBAUFabXre3l5MXLkSH788Uc+//zzQuMA8iwvExMTDh8+nOvOz8PDg2effZaNGzfy/PPP57re77//Tp8+fbS2z507FxsbGwIDA7US/eDBgzU/vmbOnAlAly5d2Lt3r9Z7fvLkyXz77bcsXryYCxcu4ObmVuDrAvJsI83PzJkzmTVrVoHHREdHA2j9XdSMjY2pVauW5piCuLi48MUXX7Bo0SJefPFFzXZra2t++eUX+vfvX+D5Fy9e5MKFC/Tu3RsHB4c8j2natCmA1mdDVzY2Nvz222+axDl48GAGDhzIqlWrtJL2p59+WmBnsic5ODiwb9++Ao/x9/fH0NAw3x8mADNmzOD48eOax66urnzwwQfUqlWrSHFURJK0K5FFfy/i0oNL+g4jt8THsZU0af/222/89ttvWtu6d+/ORx99hImJyf+eLvHxj5ai9O62srLi4cOHmsdJSUnUrl27RHECXLp0iYiICLy8vPL88VDSGo++ffvSqlWrXNW9Xl5ebNu2jc2bN2u+0BVFYcuWLTRt2lRzB5GYmMj+/fsZPnw4FhYWWneVNWrUoEOHDhw6dKjQONTn5bXakUql0iRs9WpxWVlZODs7Y2Vlxblz53Kd4+zsnCthR0REEB4ezosvvkhOTo5WrI0aNaJx48YcPnxYk7Sf/JGgvovKycmhZ8+eLF68mH/++adISfuPP/4o9Jgn4yhMamoqgNZ79UmmpqZFrr6vW7curVq1YuTIkbi5uREXF8fatWt57bXX+OGHH+jbt2++5/r7+wMU2Ftd/fd88rOhq2eeeUbr/V6/fn2aNWuWa4nf559/PlcNUX4K69197tw5Ll++TP/+/fNsHlObPXu2ZpnjI0eOcP36dc33R2UlSbuSUBSFV9xe4dPUT0nOqliLfViZWvF2j7dLfB31HWB2djbXr1/n999/1yyR9yR1sk5KSir0muqhXE+eWxqLpai/kFxcXEp8rbyo74T+q3v37tSvX5/g4GBN0j527BhRUVG88cYbWvHl5OSwdevWfNsXi/PDQlGUPLfv3r2bpUuXcv78eTIzM7X2xcXF5To+r9d17do1IO8fbWpPJs3s7GyWLl1KcHAwN27cyFWroW4TLkyPHj2KdFxRqd+n6hkI/ys9PZ2aNWsWep1Dhw7xwgsv8MEHHzBhwgTN9pEjRzJy5Ejee+899u3bl+ePg7S0NLZt24adnV2Bd+Tqv2dp1NDl9YPG1taWqKgorW0tW7akZcuWJX4+eFz9DwX/MIHHd9Zqo0aNYtmyZUyfPp01a9ZU2ipySdqVQE5ODvHx8QywH8CAsQOoXbt2pR5nmJ9GjRppvkh79+5Nr169GD16NG+88QZr1qzRHKcel3r+/PkCr3f9+nVSUlK07oQdHR05fvw4N2/eLHEVeUllZWXluy+/9lMDAwNGjx7N4sWLOXv2LB06dCA4OFizXU39pTx06NBijQ/+L3U1Yl4dd/bs2cPMmTNxdXVl7ty51K9fX3MX/Prrr+eZ6PN6XerjCuqw9+T7feHChaxcuZLBgwczffp0ateujbGxMTExMcyZM6fITRP3798v0nHwuHbCwsKiwGPU1eJ5VYFnZmYSGxtbpB95fn5+5OTk5BoTb2pqSr9+/VixYgX//vsvzs7Ouc4NCQkhMTGRCRMmFLjokPoHVWnUOhX1x19iYmKRaxoMDQ3zrcJOTk5m+/bt1KtXr8Aah7yMGTOGL774An9/f0naomxkZWXx6NEjzV2MtbV1vtVvVU3z5s2ZOnUqv//+O9u3b2f48OHA4/auNm3acObMGS5dupTnlxfAhg0bgMdtbGpDhgzh+PHjbNiwgXfeeUfn2Jo1awY87kBVGFtbW62JH9Ru376t03N7enqyePFigoODcXR0JDQ0lB49emi1pTZu3BgDAwPS0tJKdEfZqlUrIHfvfYBNmzZhamrK6tWrtZJxSkpKke92Qfvuuyixbt68maeeeooffvhBa/vBgweL/JwAvXr1KvKxRWnTdnR0xMzMjLNnz+baFxYWRnZ2Nu3atSv0udRJP68fH+ofevn94PP390elUuHj41Pgc6j/niXthFYcCxYsKJU27R07dpCcnMzUqVOLvfa3uiOc9B4XZSI9PZ1Hjx6Rk5ODgYEBNWvWrJJ32AV57rnnWLNmDT/88ANDhgzRfEjffvttpk2bxhtvvMHy5ctzDY/Zs2cPq1atomXLllodVcaOHcu6detYvnw5rq6uDBs2LNdzRkZGEhQUxCuvvJJvXM7Ozjg6OrJlyxaefvrpPDuiqe9AmjVrRmhoKOfOndP60l66dGnxC4THSa5jx47s2LEDFxcXUlJStDqgAdSsWZO+ffty8OBBjh49Srdu3XJd58GDB9jZ2RX4XK1bt8bS0jLPXs8GBo87QP43ufzyyy/F6ojn4uKCk5MTGzduZMKECbRo0UJrv6IoPHr0SHPnldedXWZmJkuWLCnyc0Lpt2mbm5szcOBAtm7dSmhoqNawr+XLl2NoaKj54al27do1jI2Nady4sWZbq1atuHHjBkFBQUyfPl2zPSEhgd27d1OjRg3Nj6n/Xuv06dN069ZN63p5Uf+wUA+pLA+l1aZdWM/4rKwsEhMT82yKWLFiBQAdO3YsUhwVkSTtCiolJUVTlWRsbEzNmjUr3Lqu5aFmzZpMmjSJJUuWEBwcrPmgdu/enY8//piPP/6Y4cOH55oRbd++fTRt2pTFixdrfQGYmpqyePFiXnrpJV5//XXWrVtH7969NTOinT59mn379hX6ZaZSqfj888+ZOnUqEyZM0Az5Sk1NJSwsjIYNG/L224/b+X19fVm2bBkzZsxgypQpmJubc+DAgRJ1iPHy8uL9999n0aJFWFtb4+HhkeuYjz/+mAkTJjBt2jRGjhyJq6srBgYGREVFcfDgQdq2bcvChQsLfB5DQ0MGDx5MSEgIKSkpWmO1hwwZwq5du5g8eTKenp4oisKhQ4e4du1akdpu1VQqFV9++SVTp07F09MTLy8vWrVqRVZWFpGRkezduxdPT0/Nne6QIUNYt24dr7zyCj179iQ+Pp4tW7YUe36C0m7TBnjjjTc4cuQI77zzDhcuXKBhw4bs3buX/fv38+KLL9K8eXOt44cNG5brrvLFF1/k0KFDfP3110RERNCpUyfi4+Px9/fn3r17zJ07N8+kpm7n9fX1LTTOAwcO0LJly1x32u+++y6bNm1i5cqVpZ7QS6NN+/Lly4SFhRXYMz4lJYU+ffrg4eGBo6MjdnZ2xMbGcuDAAU6fPk3r1q2ZNGlSieLQp+qXBSoJ9d1EjRo1sLa2rnLjr4vj2WefZfXq1fzyyy+MGjVK0zwwbtw4OnXqxB9//MG+fftYv349xsbGNGvWjDfffJNJkyblOfd4w4YNCQwMZOPGjezcuZOlS5eSlJSkmXt89uzZeHt7FxqXq6srQUFB/Prrr+zdu5fAwEBsbGxwdnbWqnp1cHBg8eLFfPPNN/zwww9YWVkxaNAg3nrrLZ566imdymTYsGF89tlnJCUl4evrm+eXeL169QgODsbPz489e/awY8cOjI2NqVevHk899VSBQ2WeNHHiRAIDAwkNDWXMmDFaMaSkpLBixQoWLVqEhYUFPXr0YO3atTz99NPFej3Ozs5s3ryZJUuW8Oeff7Jx40bMzc2xt7fH3d2doUOHao6dM2cOlpaW7Nixg3379lG3bl1GjBjB6NGj86w5KU8NGjRg/fr1fPvtt6xfv14z9/jHH39cpGQKj4cybtiwgcWLF3PkyBG2b9+OmZkZrVu3Zvbs2VrNPWoZGRls2rSJmjVr5vkD7kk3btzg7NmzvP/++7n2JScno1KpqFOnTtFecDkrSgc0MzMzJk2axMmTJzly5AiJiYmYmZnRokUL3nnnHSZOnFipJ6BSKfl1CxV5+ueff/j30b8Y2hkyonXeM2rpKicnh4yMDK5fv06zZs0wMDDA2Ni40s/3nJ2dTVpamtZ4Y1G4ilRuL7/8Mnfv3iU4OLhCvx8rUplVVB9++CEHDhxg586dmr4I2dnZpKSk4O7uTv/+/fniiy/0HKV+paWlab6H80vw586dQ6VSFTpfRGmrvrdvFczVq1f55ZdftKpMTUxMKvQXpKg+5syZw9WrVzUzyonKKSoqisDAQN54441cPfnPnz9Penq61mIkouKR6nE9UxSFw4cPa9q0Tp48me8YXSH0pUmTJoUOsRMVn4ODQ75/x3bt2nH69GmpoajgJGnrUXp6Ops3b9YMG3Jzc6N37946DwUSQghRtUnS1pMHDx6wYcMGHjx4gKGhIcOGDaNTp056XaVICCFExSZJWw+ioqJYtWoV6enpWFlZMW7cOM2ScUIIIUR+JGnrQd26dalZsyYmJib4+PgUaeELIYQQQpJ2OUlPT9f0Bjc2NmbixImYm5vn2+lDRuIJIYR+VOTvXxnyVQ7u3bvHkiVLtJZCtLS0zDNhq7f9d8UkIYQQ5UM9t3tFnIVSknYZu3jxIn5+fsTGxnLmzJl8l+1TMzY2xtTUlPj4+Ar9a08IIaqqhIQEDA0NK+Twt4r3M6KKyMnJYd++ffz111/A40UjvL29i7RCl52dHVFRUURGRmJjY1PpZ0XLzs7WrK5TET8EFZWUW/FJmelGyu0xRVFITk4mISGB+vXrV8jvXUnaZSAlJYWgoCCuXbsGPF7cwsPDo8jzh1tbWwOPh4X9dyH5yignJ4esrCyMjIyq9RzqxSXlVnxSZrqRcvsflUqFra0tNjY2+g4lT5K0S1l2djZ//PEHDx48wNjYmFGjRuHq6lrs61hbW2NtbU1mZibZ2dllEGn5SU1N5d9//6Vx48a5pk4U+ZNyKz4pM91Iuf2PsbFxha5tkKRdygwNDenevTuHDx/G19eXevXqleh6xsbGGBsbl1J0+qFeW9nU1LRSr65T3qTcik/KTDdSbpVHhU3aoaGh+Pn5ERERgbGxMW5ubrz22ms4OzsX6fxHjx7x3XffsXfvXuLi4nBwcMDb25tp06aVeo/AnJwcEhISsLW1BaBTp060bdu20idbIYQQFUuFbLwICAhg1qxZpKam8tZbb/Hyyy8TERHBhAkTuHTpUqHnJyUlMWnSJAICAhgyZAgffvghHTp04Ouvv2bu3LmlGmtycjKrVq1i+fLlpKSkaLZLwhZCCFHaKtyddkJCAgsXLsTe3p5169ZpZgsbNmwYw4YNY/78+axevbrAayxdupSrV68yZ84cnn32WQB8fHywsbFhxYoVeHt7061btxLHeufOHTZs2EBCQgImJibcu3dPVugSQghRZircnfaePXtISkrKNb2nvb09Q4cO5cSJE0RGRhZ4jc2bN2NmZsaECRO0tk+bNk2zv6Qe3HjAsmXLSEhIoHbt2jz//POSsIUQQpSpCpe0w8LCAOjYsWOufept586dy/d89TApFxeXXB0q7O3tqV+/vuY5dFWDGtw8cZPs7GwcHR15/vnnqVOnTomuKYQQQhSmwlWPx8TEAI8T7H+pt6mPyUt0dHS+56u3R0RE6BxfZmYmtua29O/fHzMzM8zMzEp0vepAPbPblStXKuRkBRWVlFvxSZnpRsqt+DIzM/VSVhUuaaempgLkOXOYqamp1jF5Ua9Hnd/MYyYmJiVas1qlUmFoaEjNmjV1vkZ1o1KpijQTnNAm5VZ8Uma6kXIrPpVKJUkb0Azsz2uObnWyLWjwv7pKPL85vtPT00s0DjGvanshhBCiPFS4Nm31ZCTqau4nqavFC5qwRF0tntf56mvkV3UuhBBCVGQVLmm3a9cOgDNnzuTap97Wtm3bfM+3s7OjQYMGhIeH56oGj46O5u7du5rnEEIIISqTCpe0PTw8sLCwICAggKSkJM326OhoQkJCcHNzo1GjRgAkJiZy7do1YmNjta4xatQo0tLSWLdundb2P/74A4DRo0eX8asQQgghSp9KqYCLNm/YsIEPPvgAR0dHfH19yczMZNWqVTx69Ig1a9bQunVrAIKCgpg7dy4zZ85k1qxZmvOTkpIYO3Yst27d4umnn8bJyYlTp04RHBzMiBEj+Prrr/X10oQQQgidVbiOaAC+vr7Y2NiwdOlSFi1apJl7/PXXXy/S3OOWlpasXbuW7777jp07d7J+/XocHBx44403NBOsCCGEEJVNhbzTFkIIIURuFa5NWwghhBB5k6QthBBCVBKStIUQQohKQpK2EEIIUUlI0hZCCCEqCUnaQgghRCUhSVsIIYSoJCrk5Cr6EBoaip+fHxEREZrJXF577bUiTeYC8OjRI7777jv27t1LXFwcDg4OeHt7M23aNIyMqmYx61pmiqKwdetWDhw4wPnz54mJicHGxoZWrVoxbdo0evbsWU6vQD9K+l570po1a/jkk08AOHjwYJVdDKc0yuzIkSP88ccfhIWFkZKSgp2dHe3atWPBggVYWlqWYfT6U9JyO3PmDL/99hvh4eE8evQIOzs73NzcmD59Oo6OjmUcfflbsmQJFy9e5OLFi9y6dQsDAwMuXrxY7OuUZT6QyVWAgIAA5s2bp5k2NSMjg9WrV/Po0SPWrVtX6Bs8KSkJX19frl+/rpk29eTJk2zatIlRo0axaNGicnol5ackZZaenk67du1wcnKif//+NGzYkPv377N+/XpiYmJ48803eeGFF8rx1ZSfkr7XnhQdHc2wYcNQFIWUlJQqm7RLo8z8/PxYtGgRXbp0YcCAAVhaWnL//n3OnDnDZ599Rp06dcrhlZSvkpbb3r17mTlzJo0bN8bLy4tatWpx/fp1/P39yczMZMOGDTr90KzInJycsLa2xsXFhX///ZfY2NhiJ+0yzwdKNRcfH6906tRJ6dOnj5KYmKjZfvfuXaVjx47KxIkTC73Gd999pzg6OirLli3T2r5gwQLF0dFROXLkSKnHrU8lLbPMzMw8y+TevXtKly5dlDZt2ihxcXGlHre+lcZ77Ukvvvii4unpqbz11luKo6Ojcvfu3dIOWe9Ko8yOHTumODk5KYsWLSrLUCuU0ii38ePHK66urkpsbKzW9j179iiOjo7KggULSj1ufbt586bm35MmTVJcXFyKfY2yzgfVvk17z549JCUl4ePjo1VFZm9vz9ChQzlx4gSRkZEFXmPz5s2YmZkxYcIEre3qec43b95c+oHrUUnLzMjIiG7duuXaXqdOHTp37kxmZibXr18vk9j1qTTea2o7duzg4MGDfPzxxxgaGpZVyHpXGmX266+/YmtryyuvvAJAcnIyWVlZZRq3vpVGuSUmJmJmZoaNjY3WdnVtjrm5eekHrmeNGzcu8TXKOh9U+6QdFhYGQMeOHXPtU287d+5cvuc/ePCAqKgoXFxcMDMz09pnb29P/fr1Nc9RVZS0zAoSExMDQO3atXWMruIqrXKLj49nwYIFTJo0qcC15auCkpZZamoqx48fp0OHDmzfvh13d3c6depE+/btefbZZ7l06VLZBK5npfFe69mzJwkJCcyePZvw8HBiYmI4cuQI77//PvXq1ePpp58u/cArufLIB1Wzh1QxqJNEXm2B6m3qY/ISHR2d7/nq7RERESUNs0IpaZnl5+DBg5w7d46nnnpKs2Z6VVJa5bZw4UKMjIx49dVXSzfACqikZXbz5k2ysrI4d+4chw8f5rnnnsPV1ZWLFy/y+++/M2HCBAIDA2nevHnZvAA9KY332muvvUZCQgJbt25ly5Ytmu2dOnUiMDCwSvYDKKnyyAfVPmmnpqYCYGJikmufqamp1jF5SUtLy/d89Xb1MVVFScssL//++y/vvPMOFhYWzJ8/v+RBVkClUW5HjhwhKCiIn3/+ucr2eH5SScssKSkJgIcPH/LJJ5/g6+sLwMCBA2nQoAHz5s3j559/5uuvvy7t0PWqNN5rxsbGNGnShPbt2zNs2DDs7e25dOkSy5Yt48UXX2TZsmXY2tqWeuyVWXnkg2qftNXtMhkZGbn2qQu3oLYbdRVIXufD457S/60mqexKWmb/dfv2baZNm0ZGRgZLliyhWbNmpRNoBVPScktNTeX999/H3d0dDw+Psgmygimtz6eBgQGenp5a+8aMGcOHH37I0aNHSyvcCqM0PqOzZ8/m4MGDhISEaO6q3d3d6dixI88++yy//vorc+fOLeXIK7fyyAfVvk27Xr16wP+qNZ6krj5SH5MXdTVIXuerr1HVhuGUtMyeFBkZydSpU4mPj+e3336jc+fOpRdoBVPScvPz8yM6Oppp06YRGRmp+S8lJUVz3aJ2ZKssSlpm9evXB8Da2jrX3Y+xsTE1a9YkPj6+tMKtMEpabnfv3mXbtm089dRTuarBe/TogbW1NceOHSvFiKuG8sgH1T5pt2vXDng8icB/qbcV1NnHzs6OBg0aEB4enqvaIzo6mrt372qeo6ooaZmpRUVFMWXKFB49esTvv/9Oly5dSjfQCqak5RYVFUVmZiYTJ07E3d1d89+uXbsA8PX1xd3dvUr1jC5pmdWuXZuGDRsSHx+v+XGjlp6eTmxsbJXs9FjSclMnnezs7Fz7FEUhOzs7z33VXXnkg2qftD08PLCwsCAgIEDT/gWPCzgkJAQ3NzdNp6jExESuXbtGbGys1jVGjRpFWloa69at09r+xx9/ADB69OgyfhXlqzTKLCoqismTJxMXF4efnx9PPfVUub4GfShpuU2ePJmff/45139du3YFYP78+fz8889VaghYabzXPD09URSFNWvWaG1fs2YNOTk59OvXr8xfR3krabk1a9YMQ0NDTp48ye3bt7WuHRISQnJycpW7GSkuveWDEo3yriLWr1+vODo6KiNGjFBWrVqlLFu2TOnfv7/SoUMH5cKFC5rjAgMDFUdHR+WHH37QOj8xMVEZPHiw4uLionz66aeKv7+/Mnv2bMXR0VF54403yvvllIuSlFliYqIyYMAAxdHRUfn444+VTZs25frv1q1b+nhZZa6k77W8qN9rVXFyFUUpeZklJycrI0eOVJycnJR3331XWbt2rTJv3jzF2dlZ6du3r3L//v3yfknloqTl9sUXXyiOjo5Kt27dlO+++05Zv3698uGHHypt2rRRunTpojURSVURHBys/Pzzz8rPP/+s9O/fX3F2dtY8/vnnn7WO1Vc+qPYd0eBxtaKNjQ1Lly5l0aJFmjl6X3/99SJN02dpacnatWv57rvv2LlzJ+vXr8fBwYE33nhDM6C+qilJmcXFxWnaXtesWZPrDgjg888/r5LDvkr6XquOSlpmNWrUYPXq1fz000+EhoayefNmbG1t8fb25pVXXsHOzq4cXkX5K2m5vf322zRv3pyNGzeyfPlyMjIyqFWrFsOHD2fmzJlV8vMZGBjI8ePHtbZ9//33mn/PmDGj0GuUdT6QuceFEEKISqLat2kLIYQQlYUkbSGEEKKSkKQthBBCVBKStIUQQohKQpK2EEIIUUlI0hZCCCEqCUnaQgghRCUhSVsIIYSoJCRpC1GIyMhInJycmDNnjr5DqTDmzJmDk5NTsVYVO3bsGE5OTvz4449lGJkQVZtMYyqqFCcnpwL3f/7553h5eZVTNKUvKCgo1xrGxsbG1K1bl65du/L888/TokULvcQWGRmJu7s7np6eLFy4UC8xFNd/3y8GBgZYW1vj6OiIt7c3o0ePRqVSleg51H+zyv7eExWDJG1RJc2cOTPP7S4uLuUcSdlwdnbGw8MDeLza0PHjxwkKCiIkJISVK1eW+QpMb7zxBtOnTy/yuunweLnIHTt2ULNmzTKMTDfq90tWVhY3b95kz549HD9+nPPnzzNv3jw9RyfE/0jSFlXSrFmz9B1CmXJxcdF6jYqiMHfuXIKDg/n6669ZsWJFmT5/3bp1qVu3brHOMTc311stQGH++345deoUkyZNYvXq1TzzzDM0bNhQT5EJoU3atEW1ExMTw08//cT48ePp2bMnrq6u9OrVizfeeIMrV64U+Tr379/n888/Z/DgwXTo0IFOnToxcOBA3nnnHW7dupXr+EOHDjF9+nS6du2Kq6srHh4efPHFFyQkJJT4NalUKp5++mkAzp07p9mek5PDmjVr8Pb2pmPHjnTo0AEvLy/NWtL/dez/tXfnQU1dewDHv2FRQKMWpKFuY7VGVBgkVTatUqPUDRVcu1AddIDaxVq0ajudzrhPW2lHlNalLqCCCyCMViuLCCpSaKU1WGqt+5K2YkAKWhO57w9f7jMkRXj6Zh5yPjP5I+ece+49N5n87llublER0dHRDBkyBC8vLwICApg4caLVPHT9Oe34+Hi0Wi0A6enp9O7dW36lpaXJddef0x45ciReXl5WzyQ2S0hIoHfv3lZPgtPr9SxevBitVouXlxf+/v7ExMRYtP1RPP/88/Ts2RNJktDpdBZ5Op2OpUuXMm7cOPz8/PD29iYkJIQVK1ZQWVlpUTYiIkKezli0aJHFeXlwPYDJZGL79u1MmTIFjUaDj48PEyZMYNu2bTY/J6HlEj1tocUpKSlhw4YN+Pv7ExISgrOzMxcvXuTbb78lNzeX5OTkhw6j3759m2nTpnHlyhUGDRrEsGHDkCSJa9eucfjwYUaNGkW3bt3k8mvWrCE+Pp4OHToQHByMq6srZ86cYdOmTeTn55OSkoJSqXykdtl6YF9sbCzffPMNnTp1YtKkSSgUCrKzs1m8eDElJSV8/vnnctkjR44QHR2NUqlk2LBhqFQqKisrOXfuHMnJyQ2OXvj5+fH666+TmJhoMXQPDU9JhIWFERcXx/79+4mIiLDKz8jIwNHRkdGjR8tpZWVlREZGUlVVxeDBgwkJCcFgMJCdnc0rr7zC2rVrGTp06EPP18OYg6WDg+XP5K5du8jOzmbgwIEEBQVx7949dDodW7Zs4ciRI+zZs4e2bdvK7VMqleTk5KDVai3ORbt27QAwGo3ExMRw9OhRevTowdixY2ndujVFRUUsWbKE0tJSPvvss0duj/CEeOQncgvC/xG1Wi0/mL7+KzU1VZIkSbpx44ZUXV1tta1Op5N8fHykyMhIi/TLly9LarVaWrBggZyWnZ0tqdVqaenSpVb1/P333xb1FxYWSmq1Wpo2bZp069Yti7Kpqan/WI8t5vIPHovZwoULJbVaLUVEREiSJEmZmZmSWq2WwsPDpZqaGrlcTU2NFBYWJqnVaikjI0NOf/PNNyW1Wi2dPn3aqu6KigqL9wsWLJDUarV0+fJlOc3WeXrQiRMn5M/G7Pr165Knp6cUFhZmVf7kyZOSWq2W3nrrLTnNaDRKw4cPl7y9vaXi4mKL8nq9Xho8eLAUFBQk3blzx+Yx1Gf+vtRXXFwseXp6Sv369ZP0er1F3pUrVySTyWS1TXJysqRWq6V169ZZpJs/M/P3r77Vq1fL34EH6zWZTNKiRYsktVotZWVlNao9wpNP9LSFJ9KaNWus0vz8/AgPD8fNzc3mNv369SMgIIBjx45hNBpxdHT8x/rNK4qdnZ2t8lq1akWrVq3k90lJSQAsWbLEqjcdHh5OYmIi+/bt48MPP3x4w/7t559/loeZq6urKSkpoaysDCcnJ9577z0AUlNTgfu9bRcXF3lbFxcXYmNjiYyMZM+ePYwbN86iTU5OTlb7c3V1bfSxNYWHhwcBAQEcP36cX3/9lV69esl56enpAEyYMEFOy8vL49KlS8ycOZMBAwZY1KVSqZg1axbLly+nsLCQ4ODgRh+H+VyaTCYuXbpEVlYWkiQxf/58q8V2nTt3tlnH1KlT+fTTTzl69ChRUVGN2m9dXR3btm3D3d2dhQsXYm9vL+fZ29uzcOFC0tLSyMzMtBi9EFouEbSFJ9Ivv/zSYH5eXh4pKSnodDoMBgMmk8ki32AwNLjQys/PD5VKxfr16zl9+jRDhw7F19eXPn36WPzwApSWluLo6MiBAwds1mU0Grl58yYGg6HRK6vLy8spLy8H7t/y5e7uzvjx44mKiuK5554D7gd2Ozs7Bg4caLW9v78/9vb2nD59Wk4LDQ3l0KFDTJkyhdGjR+Pv749Go8HDw6NRx/TfCgsL4/jx46Snp/P+++8DcPfuXQ4ePIirq6vFUHdpaSkAV69etXm/94ULFwA4d+5ck4J2/Ys8hULB8uXLbd6iZTQa2blzJ/v37+e3336jurraYt75999/b/R+z58/T2VlJd27dychIcFmGScnJ86fP9/oOoUnmwjaQouTmJjIsmXLaN++PUFBQTzzzDM4OzvL873l5eXcvXu3wTratm3Lrl27WL16Nbm5uRQUFAD3e6SvvvoqMTEx8lxoZWUlJpPJZu//QbW1tY0O2o25F7q6upr27dvbHDFwcHDgqaeeoqKiQk4LCQlh3bp1bNq0idTUVFJSUgDw8vJi3rx5BAYGNurYmmrEiBG0adOGzMxMYmNjsbe3Jzc3l8rKSqZPn24xp2xe6HXw4MEG66ytrW3SMZgv8mprazl58iQffPABH3/8MV26dMHPz8+i7Ny5c8nKyqJr165otVo6duwoj6xs3boVo9HY6P2a23PhwoUGvx81NTVNao/w5BJBW2hRTCYT8fHxuLu7k5aWZtWbNvfkGsPDw4Ply5cjSRJnz57lxIkTbN++nfj4eOrq6njnnXeA+wFekiS+++67x9mUh1IqlVRVVdkc6jeZTBgMBnnBlFlwcDDBwcHU1tby448/kpeXR3JyMlFRUezdu/d/csuWs7MzI0eOJDU1lWPHjjFkyBD27t0L3L84qd8muL+q3Lxa/XFycXFh0KBBrFu3jvDwcObPn8/BgwflaZBTp06RlZVFYGAgGzZssDivdXV1bNy4sUn7M7dnxIgRD72oEwQQt3wJLYzBYODWrVv4+vpaBeyamhrKysqaXKdCoaBXr15ERESwefNmALKysuT8/v37U1VV1aTbyR6HPn36UFdXR0lJiVVecXEx9+7do2/fvja3dXFxITAwkEWLFhEdHc3du3fJz89vcH/maYF79+41+VjNwXnv3r3cvHmTgoICevfubbXy3MfHB8Bmmx4nT09PJk+ejF6vZ8uWLXK6+VY+rVZrdSH0008/cefOHau67Ozu/8zaOi89evSgXbt2lJaWNqmHLrRcImgLLYqbmxvOzs7odDqLIUej0ciyZcswGAyNqufMmTM2/3f7xo0bALRu3VpOmzFjBgAfffSRzfnO2traJvXwG2vixIkArFq1itu3b8vpt2/fZtWqVQBMmjRJTi8sLLQZdMxD6A8urrOlXbt2KBQK9Hp9k491wIABdO3alZycHHbs2IHJZLI5n6zVaunWrRs7duzgyJEjNus6efKkRXv/W7Nnz6Z169Zs2rSJqqoq4D+L0OqPmlRUVLB48WKb9ZinPGydFwcHB1577TX+/PNPli5davP8//HHH5w9e/aR2iI8OcTwuNCi2NnZERERwfr16wkNDUWr1WI0GikqKqKqqgp/f3+KiooeWs/x48dZuXIlvr6+9OjRAzc3N/R6PTk5OSgUCmbOnCmXDQwMJDY2lri4OF566SWGDBlCly5dqK2t5dq1axQXF6PRaPj6668fa1tDQ0PJycnhwIEDjBkzhuHDh8vz9leuXGHUqFHyynGAlStXcvXqVfz8/OjcuTOOjo6UlZVx4sQJOnXqxJgxYxrcX5s2bfDx8aG4uJh58+bRvXt37OzsGDZsGJ6eng1uq1AomDBhAvHx8Xz11Vc4ODgwduxYq3KOjo7Ex8cza9YsoqKi5MV/Tk5O6PV6Tp06xeXLlzl69KjNlf1NoVKpmDp1KomJiWzcuJHY2Fi8vb3RaDQcOnSIadOmodFoqKioID8/n2effdbm4sX+/fvj7OzM1q1bqayslO9eiIiIQKlUMnv2bMrLy0lJSeHw4cMEBASgUqmoqKjg4sWL/PDDD8ydO1deYCi0bCJoCy3OnDlzcHV1Zffu3ezcuROlUklQUBDvvvtuo59A9cILL3D9+nWKi4vJycnhr7/+4umnn2bQoEHMmDEDjUZjUT4qKgqNRkNSUhLff/89ubm5tG3bFpVKxZQpU2wGqMchLi6OgQMHkpqays6dOwHo2bMnkZGRvPzyyxZlo6Ojyc7ORqfTUVhYiEKhoFOnTsTExDB9+nQ6dOjw0P198sknrFixgoKCAvbt24ckSXh4eDw0aAOMHz+eNWvWYDQaefHFF+nYsaPNcp6enmRkZLB582by8vJIS0vDzs4Od3d3+vbty9tvv/3Y/t88Ojqa3bt3k5SUxPTp0+nYsSNffvklX3zxBfn5+SQlJaFSqZg8eTJvvPGGzQub9u3bs3r1atauXUtaWpq8SG7cuHEolUocHR1JSEggIyOD9PR08vLy5EWJXbp0Yc6cOYSGhj6W9gjNn0KSbPyNkiAIgiAI/3fEnLYgCIIgNBMiaAuCIAhCMyGCtiAIgiA0EyJoC4IgCEIzIYK2IAiCIDQTImgLgiAIQjMhgrYgCIIgNBMiaAuCIAhCMyGCtiAIgiA0EyJoC4IgCEIzIYK2IAiCIDQTImgLgiAIQjMhgrYgCIIgNBP/Al5qhW6A9vGhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfroscdiff['Difference (0,5) (rSO2 %)']\n",
    "y_true = dfroscdiff['ROSC-CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fpr, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tpr\n",
    "\n",
    "\n",
    "# Do for the Difference Across the Entire Signal, predicting ROSC ---------------\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfsig['Signal Diff']\n",
    "y_true = dfsig['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, color='green', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Entire Signal, ROC Curve, predicting ROSC')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "rocsig = pd.DataFrame(fpr, columns=['fpr, Signal'])\n",
    "rocsig['tpr, Signal'] = tpr\n",
    "\n",
    "\n",
    "# Do for the Difference Across the Entire Signal, predicting survival --------------------\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfsigsurv['Signal Diff']\n",
    "y_true = dfsigsurv['ROSC-CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, color='magenta', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Entire Signal, ROC Curve, predicting CPC 1 or 2')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "rocsig = pd.DataFrame(fpr, columns=['fpr, Signal'])\n",
    "rocsig['tpr, Signal'] = tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "770ab75a-aca9-43a2-9cde-bd1666c26aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do for the Difference Across the Entire Signal, predicting ROSC ---------------\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfsig['Signal Diff']\n",
    "y_true = dfsig['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, color='green', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Entire Signal, ROC Curve, predicting ROSC')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "rocsig = pd.DataFrame(fpr, columns=['fpr, Signal'])\n",
    "rocsig['tpr, Signal'] = tpr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5eff17-13f4-4d16-9a69-1929149f7f67",
   "metadata": {},
   "source": [
    "### Diff over ROSC for ALL cases, predicting survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "752847b4-7707-45cd-8236-a392a223e31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "diffall = []\n",
    "for i in range(len(df)):\n",
    "    if np.isnan(df['Difference (0,5) (rSO2 %)'][i]) == False:\n",
    "        diffall.append(df['Difference (0,5) (rSO2 %)'][i])\n",
    "    elif np.isnan(df['Fake Difference (0,5) (rSO2 %)'][i]) == False:\n",
    "        diffall.append(df['Fake Difference (0,5) (rSO2 %)'][i])\n",
    "    else:\n",
    "        diffall.append(np.nan)\n",
    "df['Diff over ROSC, all inc. fakes'] = diffall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "be641406-36a9-4265-ba48-24f2cb6ce262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROSC</th>\n",
       "      <th>CPC12</th>\n",
       "      <th>Diff over ROSC, all inc. fakes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ROSC  CPC12  Diff over ROSC, all inc. fakes\n",
       "0     0      0                             2.0\n",
       "4     0      0                            -4.0\n",
       "5     1      0                            21.0\n",
       "6     1      0                             7.0\n",
       "7     1      0                            15.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffake = df[np.isnan(df['Diff over ROSC, all inc. fakes']) == False][['ROSC', 'CPC12', 'Diff over ROSC, all inc. fakes']].copy()\n",
    "dffake[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d7a2f4b7-dcae-4bc9-8446-54d631ec4845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALL Cases, predicting CPC 1-2\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dffake['Diff over ROSC, all inc. fakes']\n",
    "y_true = dffake['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Predicting CPC 1-2, ALL cases')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fpr, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tpr\n",
    "\n",
    "\n",
    "# # Just ROSC cases, predicting CPC 1-2\n",
    "# # Extract the predicted probabilities and the true labels\n",
    "# y_pred = dfroscdiff['Difference (0,5) (rSO2 %)']\n",
    "# y_true = dfroscdiff['ROSC-CPC12']\n",
    "\n",
    "# # Compute the ROC curve\n",
    "# fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# # Compute the AUC (Area Under the Curve)\n",
    "# roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# # Plot the ROC curve\n",
    "# plt.figure()\n",
    "# plt.plot(fpr, tpr, color='magenta', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "# plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "# plt.xlim([0.0, 1.0])\n",
    "# plt.ylim([0.0, 1.0])\n",
    "# plt.xlabel('False Positive Rate')\n",
    "# plt.ylabel('True Positive Rate')\n",
    "# plt.title('Predicting CPC 1-2, JUST ROSC cases')\n",
    "# plt.legend(loc='lower right')\n",
    "# plt.show()\n",
    "\n",
    "# #Put into a dataframe\n",
    "# rocrosc = pd.DataFrame(fpr, columns=['fpr, ROSC'])\n",
    "# rocrosc['tpr, ROSC'] = tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "13eba6f3-b9c6-46f9-8779-feca7a5dcbab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    34.0\n",
       "44    29.0\n",
       "45    13.0\n",
       "65     6.0\n",
       "75    39.0\n",
       "Name: Diff over ROSC, all inc. fakes, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffake[dffake['CPC12'] == 1]['Diff over ROSC, all inc. fakes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c7924e3d-57ba-4148-b5c5-dd913616355e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47    44.0\n",
       "81    41.0\n",
       "Name: Diff over ROSC, all inc. fakes, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffake[(dffake['CPC12'] == 0) & (dffake['Diff over ROSC, all inc. fakes'] > 28)]['Diff over ROSC, all inc. fakes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "aa292d5c-87cb-4fdc-9dda-896a71bf11d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dffake[dffake['CPC12'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f7a0c6fc-fd26-4785-af29-2ae619dcfa06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9523809523809523"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-3/63"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5967a9ec-083c-4df9-8211-4f77432a3b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fpr, ROSC</th>\n",
       "      <th>tpr, ROSC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.015873</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.126984</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.349206</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.396825</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.460317</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.523810</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.682540</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.746032</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.793651</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.873016</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.936508</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fpr, ROSC  tpr, ROSC\n",
       "0    0.000000        0.0\n",
       "1    0.015873        0.0\n",
       "2    0.031746        0.0\n",
       "3    0.031746        0.6\n",
       "4    0.079365        0.6\n",
       "5    0.111111        0.6\n",
       "6    0.126984        0.6\n",
       "7    0.190476        0.6\n",
       "8    0.238095        0.6\n",
       "9    0.253968        0.8\n",
       "10   0.269841        0.8\n",
       "11   0.333333        0.8\n",
       "12   0.349206        0.8\n",
       "13   0.396825        0.8\n",
       "14   0.428571        1.0\n",
       "15   0.460317        1.0\n",
       "16   0.523810        1.0\n",
       "17   0.682540        1.0\n",
       "18   0.746032        1.0\n",
       "19   0.793651        1.0\n",
       "20   0.873016        1.0\n",
       "21   0.936508        1.0\n",
       "22   1.000000        1.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rocrosc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2936098d-2a74-4142-8f6d-4cf59c30fa77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7068564c-2d59-4625-96a8-0d4a8f450065",
   "metadata": {},
   "source": [
    "## Whole signal, start at min 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3d9dbc41-8295-463e-bb62-2c18932b88fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfsig = df[np.isnan(df['Signal Diff, min 1']) == False][['CASSID', 'Witnessed', 'Signal Diff, min 1', 'ROSC']].copy()\n",
    "dfsigsurv = df[(np.isnan(df['Signal Diff, min 1']) == False) & (np.isnan(df['ROSC-CPC12']) == False)][['CASSID', 'Witnessed', 'Signal Diff, min 1', 'ROSC', 'ROSC-CPC12']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c96a880-311e-4f41-9887-c2a6ef708a4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CASSID</th>\n",
       "      <th>Witnessed</th>\n",
       "      <th>Signal Diff, min 1</th>\n",
       "      <th>ROSC</th>\n",
       "      <th>ROSC-CPC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20009354</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20009383</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20009431</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20009463</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20009519</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20009847</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20009911</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20010026</td>\n",
       "      <td>0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>20010250</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>20010263</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20010560</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>20010591</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20010649</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>20010646</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>20010719</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>20010957</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>20010997</td>\n",
       "      <td>1</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>20011016</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>20011043</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>20011076</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>20011108</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>20011111</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>20011157</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>20011198</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>20011313</td>\n",
       "      <td>1</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>20011382</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>20011430</td>\n",
       "      <td>0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>20011442</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>20011463</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>20011492</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>20011515</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>20011601</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CASSID  Witnessed  Signal Diff, min 1  ROSC  ROSC-CPC12\n",
       "5   20009354          0                40.0     1         0.0\n",
       "6   20009383          0                10.0     1         0.0\n",
       "7   20009431          1                35.0     1         0.0\n",
       "10  20009463          1                18.0     1         0.0\n",
       "11  20009519          1                35.0     1         0.0\n",
       "17  20009847          0                19.0     1         0.0\n",
       "18  20009911          1                13.0     1         0.0\n",
       "22  20010026          0                87.0     1         0.0\n",
       "31  20010250          1                37.0     1         0.0\n",
       "32  20010263          1                18.0     1         0.0\n",
       "38  20010560          1                 5.0     1         0.0\n",
       "39  20010591          0                28.0     1         0.0\n",
       "41  20010649          1                25.0     1         0.0\n",
       "43  20010646          1                20.0     1         0.0\n",
       "46  20010719          1                 3.0     1         0.0\n",
       "58  20010957          0                -4.0     1         0.0\n",
       "63  20010997          1               -12.0     1         0.0\n",
       "65  20011016          1                 9.0     1         1.0\n",
       "66  20011043          0                42.0     1         0.0\n",
       "69  20011076          0                32.0     1         0.0\n",
       "71  20011108          0                24.0     1         0.0\n",
       "72  20011111          0                -2.0     1         0.0\n",
       "74  20011157          0                35.0     1         0.0\n",
       "75  20011198          1                28.0     1         1.0\n",
       "77  20011313          1                73.0     1         0.0\n",
       "79  20011382          0                 5.0     1         0.0\n",
       "80  20011430          0               -11.0     1         0.0\n",
       "81  20011442          0                 4.0     1         0.0\n",
       "83  20011463          1                40.0     1         0.0\n",
       "85  20011492          1                16.0     1         0.0\n",
       "86  20011515          1                 5.0     1         0.0\n",
       "90  20011601          1                14.0     1         0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsigsurv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "812f2854-49e0-4391-b6b3-764237347e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65     9.0\n",
       "75    28.0\n",
       "Name: Signal Diff, min 1, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsigsurv[dfsigsurv['ROSC-CPC12'] == 1]['Signal Diff, min 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1033e35a-bf57-40a8-861a-b1b6640b5add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do for the Difference Across the Entire Signal\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfsig['Signal Diff, min 1']\n",
    "y_true = dfsig['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, color='green', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Entire Signal, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "rocsig = pd.DataFrame(fpr, columns=['fpr, Signal'])\n",
    "rocsig['tpr, Signal'] = tpr\n",
    "\n",
    "\n",
    "# Do for the Difference Across the Entire Signal, predicting survival --------------------\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfsigsurv['Signal Diff, min 1']\n",
    "y_true = dfsigsurv['ROSC-CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, color='magenta', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Entire Signal, ROC Curve, predicting CPC 1 or 2')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "rocsig = pd.DataFrame(fpr, columns=['fpr, Signal'])\n",
    "rocsig['tpr, Signal'] = tpr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3367f4e9-34cc-4197-9972-45ba37400a96",
   "metadata": {},
   "source": [
    "## Whole signal, start at min 1, witnessed only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d106bc40-677a-4ff3-8fee-c0ff6ed82c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfsig = df[(np.isnan(df['Signal Diff, min 1']) == False) & (df['Witnessed'] == 1)][['CASSID', 'Witnessed', 'Signal Diff, min 1', 'ROSC']].copy()\n",
    "dfsigsurv = df[(np.isnan(df['Signal Diff, min 1']) == False) & (df['Witnessed'] == 1) & (np.isnan(df['ROSC-CPC12']) == False)][['CASSID', 'Witnessed', 'Signal Diff, min 1', 'ROSC', 'ROSC-CPC12']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e12e3c6f-8cfd-4e7b-9f9a-67bd22a872cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do for the Difference Across the Entire Signal\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfsig['Signal Diff, min 1']\n",
    "y_true = dfsig['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, color='green', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Entire Signal, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "rocsig = pd.DataFrame(fpr, columns=['fpr, Signal'])\n",
    "rocsig['tpr, Signal'] = tpr\n",
    "\n",
    "\n",
    "# Do for the Difference Across the Entire Signal, predicting survival --------------------\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfsigsurv['Signal Diff, min 1']\n",
    "y_true = dfsigsurv['ROSC-CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, color='magenta', lw=2, label=f'ROC curve (area = {roc_auc:.2f}), n={len(y_pred)}')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Entire Signal, ROC Curve, predicting CPC 1 or 2')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "rocsig = pd.DataFrame(fpr, columns=['fpr, Signal'])\n",
    "rocsig['tpr, Signal'] = tpr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a099b7-9824-4be3-9524-345761acf169",
   "metadata": {},
   "source": [
    "## Logit Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2cfb5dc2-5e13-458a-b2f3-18607bd8b362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.436548\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:             ROSC-CPC12   No. Observations:                   23\n",
      "Model:                          Logit   Df Residuals:                       21\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Mon, 06 Jan 2025   Pseudo R-squ.:                  0.1662\n",
      "Time:                        19:55:45   Log-Likelihood:                -10.041\n",
      "converged:                       True   LL-Null:                       -12.042\n",
      "Covariance Type:            nonrobust   LLR p-value:                   0.04540\n",
      "=============================================================================================\n",
      "                                coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------\n",
      "const                        -2.6687      1.028     -2.595      0.009      -4.684      -0.653\n",
      "Difference (0,5) (rSO2 %)     0.0811      0.044      1.829      0.067      -0.006       0.168\n",
      "=============================================================================================\n"
     ]
    }
   ],
   "source": [
    "# Assuming df is your DataFrame, and x and y are the column names\n",
    "x = dfroscdiff['Difference (0,5) (rSO2 %)']\n",
    "y = dfroscdiff['ROSC-CPC12']\n",
    "\n",
    "# Add a constant (intercept) to the independent variable\n",
    "X = sm.add_constant(x)\n",
    "\n",
    "# Fit the logistic regression model\n",
    "model = sm.Logit(y, X)\n",
    "result = model.fit()\n",
    "\n",
    "# View the summary of the model fit\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d8ef14a4-f1e3-45a6-bc6c-d6c43f431642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.436548\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:             ROSC-CPC12   No. Observations:                   23\n",
      "Model:                          Logit   Df Residuals:                       21\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Mon, 06 Jan 2025   Pseudo R-squ.:                  0.1662\n",
      "Time:                        19:55:46   Log-Likelihood:                -10.041\n",
      "converged:                       True   LL-Null:                       -12.042\n",
      "Covariance Type:            nonrobust   LLR p-value:                   0.04540\n",
      "=============================================================================================\n",
      "                                coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------\n",
      "const                        -2.6687      1.028     -2.595      0.009      -4.684      -0.653\n",
      "Difference (0,5) (rSO2 %)     0.0811      0.044      1.829      0.067      -0.006       0.168\n",
      "=============================================================================================\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>AOR</th>\n",
       "      <th>p</th>\n",
       "      <th>AOR 0.025</th>\n",
       "      <th>AOR 0.975</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Constant</td>\n",
       "      <td>0.069344</td>\n",
       "      <td>0.009455</td>\n",
       "      <td>0.00924</td>\n",
       "      <td>0.520388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Difference (0,5) (rSO2 %)</td>\n",
       "      <td>1.084458</td>\n",
       "      <td>0.067371</td>\n",
       "      <td>0.99422</td>\n",
       "      <td>1.182886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Variable       AOR         p  AOR 0.025  AOR 0.975\n",
       "0                   Constant  0.069344  0.009455    0.00924   0.520388\n",
       "1  Difference (0,5) (rSO2 %)  1.084458  0.067371    0.99422   1.182886"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(['Constant', 'Difference (0,5) (rSO2 %)'], columns=['Variable'])\n",
    "x = dfroscdiff['Difference (0,5) (rSO2 %)']\n",
    "y = dfroscdiff['ROSC-CPC12']\n",
    "\n",
    "# Add a constant (intercept) to the independent variable\n",
    "X = sm.add_constant(x)\n",
    "\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "coeffs = result.params.tolist()\n",
    "print(result.summary())\n",
    "\n",
    "#Calculate odds ratios\n",
    "oddsratios = []\n",
    "for coef in coeffs:\n",
    "    oddsratios.append(np.exp(coef))\n",
    "results_df['AOR'] = oddsratios\n",
    "\n",
    "pvalues = result.pvalues.tolist()\n",
    "results_df['p'] = pvalues\n",
    "\n",
    "# Extract the confidence intervals with alpha = 0.05 for lower and 0.95 for upper\n",
    "conf_intervals = result.conf_int(alpha=0.05)  # alpha = 1 - confidence level\n",
    "conf_intervals.columns = ['0.025', '0.975']\n",
    "lower = conf_intervals['0.025'].tolist()\n",
    "upper = conf_intervals['0.975'].tolist()\n",
    "# studylogistic['0.05'] = lower\n",
    "# studylogistic['0.95'] = upper\n",
    "results_df['AOR 0.025'] = np.exp(lower)\n",
    "results_df['AOR 0.975'] = np.exp(upper)\n",
    "\n",
    "rosc_aor = []\n",
    "for ind, row in results_df.iterrows():\n",
    "    aor = str(round(results_df['AOR'][ind], 2))\n",
    "    lower = str(round(results_df['AOR 0.025'][ind], 2))\n",
    "    upper = str(round(results_df['AOR 0.975'][ind], 2))\n",
    "    rosc_aor.append(aor + ' [' + lower + ', ' + upper + ']')\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378efdb7-0125-4a66-a02f-684d4ac35ecd",
   "metadata": {},
   "source": [
    "## Try tds Confidence Intervals\n",
    "from here: https://towardsdatascience.com/pooled-roc-with-xgboost-and-plotly-553a8169680c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26f01b4e-7629-4344-8cc1-78f6bbce179d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "# from tqdm.notebook import tqdm\n",
    "# from sklearn.model_selection import RepeatedKFold\n",
    "# import xgboost as xgb\n",
    "# from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0eb407f-e80e-4b73-8953-f043a87429f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data'\n",
    "df  = pd.read_csv(url, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb44bdfb-32db-4f9d-a898-53d34900cdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(13):\n",
    "    df[i] = df[i].apply(lambda x: np.nan if x=='?' else x)\n",
    "    df[i] = df[i].astype(float)\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07de3d4e-21b8-428e-b734-e3b3b8ee0ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "def binarize(x):\n",
    "    if x==0:\n",
    "        value=0\n",
    "    else:\n",
    "        value=1\n",
    "    return value\n",
    "df[13] = df[13].map(binarize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3943863-b121-4f1e-bd03-0c93be445fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(13, axis=1)\n",
    "y = df[13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deb699da-d514-45c1-bdb2-51b32a052104",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1bb9b18-7a26-4eee-891a-c99f2ecebc4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv    = RepeatedKFold(n_splits=5, n_repeats=100, random_state=101)\n",
    "folds = [(train,test) for train, test in cv.split(X_train, y_train)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c88feb98-051b-48d6-a140-a259120e651f",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['auc', 'fpr', 'tpr', 'thresholds']\n",
    "results = {\n",
    "    'train': {m:[] for m in metrics},\n",
    "    'val'  : {m:[] for m in metrics},\n",
    "    'test' : {m:[] for m in metrics}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7ddba4d-6868-4ac3-ae3c-a7d69efcd4e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'objective'   : 'binary:logistic',\n",
    "    'eval_metric' : 'logloss'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b0ddc9-f1ea-434c-8c99-eb4227884674",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(X_test, label=y_test)\n",
    "for train, test in tqdm(folds, total=len(folds)):\n",
    "    dtrain = xgb.DMatrix(X_train.iloc[train,:], label=y_train.iloc[train])\n",
    "    dval   = xgb.DMatrix(X_train.iloc[test,:], label=y_train.iloc[test])\n",
    "    model  = xgb.train(\n",
    "        dtrain                = dtrain,\n",
    "        params                = params, \n",
    "        evals                 = [(dtrain, 'train'), (dval, 'val')],\n",
    "        num_boost_round       = 1000,\n",
    "        verbose_eval          = False,\n",
    "        early_stopping_rounds = 10,\n",
    "    )\n",
    "    sets = [dtrain, dval, dtest]\n",
    "    for i,ds in enumerate(results.keys()):\n",
    "        y_preds              = model.predict(sets[i])\n",
    "        labels               = sets[i].get_label()\n",
    "        fpr, tpr, thresholds = roc_curve(labels, y_preds)\n",
    "        results[ds]['fpr'].append(fpr)\n",
    "        results[ds]['tpr'].append(tpr)\n",
    "        results[ds]['thresholds'].append(thresholds)\n",
    "        results[ds]['auc'].append(roc_auc_score(labels, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84ae2f95-50e6-4805-b7ed-c876e56bb9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "kind = 'val'\n",
    "c_fill      = 'rgba(52, 152, 219, 0.2)'\n",
    "c_line      = 'rgba(52, 152, 219, 0.5)'\n",
    "c_line_main = 'rgba(41, 128, 185, 1.0)'\n",
    "c_grid      = 'rgba(189, 195, 199, 0.5)'\n",
    "c_annot     = 'rgba(149, 165, 166, 0.5)'\n",
    "c_highlight = 'rgba(192, 57, 43, 1.0)'\n",
    "fpr_mean    = np.linspace(0, 1, 100)\n",
    "interp_tprs = []\n",
    "    fpr           = results[kind]['fpr'][i]\n",
    "    tpr           = results[kind]['tpr'][i]\n",
    "    interp_tpr    = np.interp(fpr_mean, fpr, tpr)\n",
    "    interp_tpr[0] = 0.0\n",
    "    interp_tprs.append(interp_tpr)\n",
    "tpr_mean     = np.mean(interp_tprs, axis=0)\n",
    "tpr_mean[-1] = 1.0\n",
    "tpr_std      = 2*np.std(interp_tprs, axis=0)\n",
    "tpr_upper    = np.clip(tpr_mean+tpr_std, 0, 1)\n",
    "tpr_lower    = tpr_mean-tpr_std\n",
    "auc          = np.mean(results[kind]['auc'])\n",
    "fig = go.Figure([\n",
    "    go.Scatter(\n",
    "        x          = fpr_mean,\n",
    "        y          = tpr_upper,\n",
    "        line       = dict(color=c_line, width=1),\n",
    "        hoverinfo  = \"skip\",\n",
    "        showlegend = False,\n",
    "        name       = 'upper'),\n",
    "    go.Scatter(\n",
    "        x          = fpr_mean,\n",
    "        y          = tpr_lower,\n",
    "        fill       = 'tonexty',\n",
    "        fillcolor  = c_fill,\n",
    "        line       = dict(color=c_line, width=1),\n",
    "        hoverinfo  = \"skip\",\n",
    "        showlegend = False,\n",
    "        name       = 'lower'),\n",
    "    go.Scatter(\n",
    "        x          = fpr_mean,\n",
    "        y          = tpr_mean,\n",
    "        line       = dict(color=c_line_main, width=2),\n",
    "        hoverinfo  = \"skip\",\n",
    "        showlegend = True,\n",
    "        name       = f'AUC: {auc:.3f}')\n",
    "])\n",
    "fig.add_shape(\n",
    "    type ='line', \n",
    "    line =dict(dash='dash'),\n",
    "    x0=0, x1=1, y0=0, y1=1\n",
    ")\n",
    "fig.update_layout(\n",
    "    template    = 'plotly_white', \n",
    "    title_x     = 0.5,\n",
    "    xaxis_title = \"1 - Specificity\",\n",
    "    yaxis_title = \"Sensitivity\",\n",
    "    width       = 800,\n",
    "    height      = 800,\n",
    "    legend      = dict(\n",
    "        yanchor=\"bottom\", \n",
    "        xanchor=\"right\", \n",
    "        x=0.95,\n",
    "        y=0.01,\n",
    "    )\n",
    ")\n",
    "fig.update_yaxes(\n",
    "    range       = [0, 1],\n",
    "    gridcolor   = c_grid,\n",
    "    scaleanchor = \"x\", \n",
    "    scaleratio  = 1,\n",
    "    linecolor   = 'black')\n",
    "fig.update_xaxes(\n",
    "    range       = [0, 1],\n",
    "    gridcolor   = c_grid,\n",
    "    constrain   = 'domain',\n",
    "    linecolor   = 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397a443f-4f2c-4126-b3db-44eb298459f4",
   "metadata": {},
   "source": [
    "# 6 - Question 1\n",
    "Does the median from the first 2 or 3 minutes predict ROSC or survival?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db5a2fb5-e127-44d8-babf-25d0e48abbf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import statsmodels.api as sm\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(5, 4)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2942154e-6d1b-48c1-8d9c-ede90df14ddd",
   "metadata": {},
   "source": [
    "### Median for first 2 minutes, relationship to ROSC and Survival with Favorable Neurological Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ca4d425a-9642-4875-b527-1f39e636bc43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['2minStartMedian']) == False][['2minStartMedian', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['2minStartMedian']\n",
    "y_true = dfstart['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprr2, tprr2, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucr2 = auc(fprr2, tprr2)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprr2, tprr2, color='blue', lw=2, label=f'ROC curve (area = {roc_aucr2:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fprr2, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tprr2\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['2minStartMedian']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprs2, tprs2, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucs2 = auc(fprs2, tprs2)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprs2, tprs2, color='blue', lw=2, label=f'ROC curve (area = {roc_aucs2:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprs2, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprs2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff321ab-f8e6-4751-9503-3d6605fca70d",
   "metadata": {},
   "source": [
    "#### Start at min 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c2cad136-cf56-497b-b68c-98aa1a240806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['2minStartMedian, min 1']) == False][['2minStartMedian, min 1', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['2minStartMedian, min 1']\n",
    "y_true = dfstart['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprr21, tprr21, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucr21 = auc(fprr21, tprr21)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprr21, tprr21, color='blue', lw=2, label=f'ROC curve (area = {roc_aucr21:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fprr21, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tprr21\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['2minStartMedian, min 1']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprs21, tprs21, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucs21 = auc(fprs21, tprs21)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprs21, tprs21, color='blue', lw=2, label=f'ROC curve (area = {roc_aucs21:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprs21, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprs21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c46b77e-dcfa-4ffc-8982-438304e968a8",
   "metadata": {},
   "source": [
    "### Median for first 3 minutes, relationship to ROSC and Survival with Favorable Neurological Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a8a06ac-a72e-4990-8dad-59b9870a72a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.0\n",
      "21.0\n",
      "45.0\n"
     ]
    }
   ],
   "source": [
    "print(df[np.isnan(df['3minStartMedian']) == False]['3minStartMedian'].median())\n",
    "print(df[np.isnan(df['3minStartMedian']) == False]['3minStartMedian'].quantile(0.25))\n",
    "print(df[np.isnan(df['3minStartMedian']) == False]['3minStartMedian'].quantile(0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f610238d-27e1-4eef-b784-3f699a324fc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33.5\n",
      "22.0\n",
      "45.0\n"
     ]
    }
   ],
   "source": [
    "print(df[np.isnan(df['3minStartMedian, min 1']) == False]['3minStartMedian'].median())\n",
    "print(df[np.isnan(df['3minStartMedian, min 1']) == False]['3minStartMedian'].quantile(0.25))\n",
    "print(df[np.isnan(df['3minStartMedian, min 1']) == False]['3minStartMedian'].quantile(0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24375800-85c6-4edf-a78c-e3fb960f996f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48bf4d26-9c45-4535-99ec-d5df49145b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['3minStartMedian']) == False][['3minStartMedian', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['3minStartMedian']\n",
    "y_true = dfstart['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprr3, tprr3, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucr3 = auc(fprr3, tprr3)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprr3, tprr3, color='blue', lw=2, label=f'ROC curve (area = {roc_aucr3:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fprr3, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tprr3\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['3minStartMedian']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprs3, tprs3, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucs3 = auc(fprs3, tprs3)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprs3, tprs3, color='blue', lw=2, label=f'ROC curve (area = {roc_aucs3:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprs3, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprs3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8995ecbf-ac76-4449-854e-37c5ba2b35bb",
   "metadata": {},
   "source": [
    "#### Start at min 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "562a28b8-0566-4ef4-9731-19fd8bcfa542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['3minStartMedian, min 1']) == False][['3minStartMedian, min 1', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['3minStartMedian, min 1']\n",
    "y_true = dfstart['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprr31, tprr31, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucr31 = auc(fprr31, tprr31)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprr31, tprr31, color='blue', lw=2, label=f'ROC curve (area = {roc_aucr31:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fprr31, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tprr31\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['3minStartMedian, min 1']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprs31, tprs31, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucs31 = auc(fprs31, tprs31)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprs31, tprs31, color='blue', lw=2, label=f'ROC curve (area = {roc_aucs31:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprs31, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprs31"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c278e00-41d2-44cf-bd31-4e0c7c1acaef",
   "metadata": {},
   "source": [
    "### Median for first 5 minutes, relationship to ROSC and Survival with Favorable Neurological Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c5bbbd94-90e1-438f-a7f2-f54ef197abcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['5minStartMedian']) == False][['5minStartMedian', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['5minStartMedian']\n",
    "y_true = dfstart['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprr5, tprr5, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucr5 = auc(fprr5, tprr5)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprr5, tprr5, color='blue', lw=2, label=f'ROC curve (area = {roc_aucr5:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fprr5, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tprr5\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['5minStartMedian']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprs5, tprs5, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucs5 = auc(fprs5, tprs5)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprs5, tprs5, color='blue', lw=2, label=f'ROC curve (area = {roc_aucs5:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprs5, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprs5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e9377278-6091-4a0a-88ac-4e995f6907c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh = 45"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60115d7a-3305-48f8-9a25-8e2e67e7768a",
   "metadata": {},
   "source": [
    "#### Start at min 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "89e8c04a-a8a4-4a49-8f0c-0e6a298ea698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['ROSC'] == 1]['5minStartMedian, min 1'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "992b374b-8009-4d18-9ba6-31c0b1ee7b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['ROSC'] == 0]['5minStartMedian, min 1'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "d8053163-3066-493c-836a-40c474c66a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['5minStartMedian, min 1']) == False][['5minStartMedian, min 1', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['5minStartMedian, min 1']\n",
    "y_true = dfstart['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprr51, tprr51, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucr51 = auc(fprr51, tprr51)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprr51, tprr51, color='blue', lw=2, label=f'ROC curve (area = {roc_aucr51:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fprr51, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tprr51\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['5minStartMedian, min 1']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprs51, tprs51, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucs51 = auc(fprs51, tprs51)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprs51, tprs51, color='blue', lw=2, label=f'ROC curve (area = {roc_aucs51:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprs51, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprs51"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351445d5-452e-42db-817d-696e9ae39079",
   "metadata": {},
   "source": [
    "## Plot on same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f0fbcf9a-7f95-4722-b371-d928a3b3e95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagnoal\n",
    "x = [0, 1]\n",
    "y = [0, 1]\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "# Plot multiple lines\n",
    "plt.plot(fprr2, tprr2, label=f'First 2 min, AUC: {roc_aucr2:.2f}')\n",
    "plt.plot(fprr3, tprr3, label=f'First 3 min, AUC: {roc_aucr3:.2f}')\n",
    "plt.plot(fprr5, tprr5, label=f'First 5 min, AUC: {roc_aucr5:.2f}')\n",
    "plt.plot(x, y, color='black', linestyle='--')\n",
    "\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('ROC, ROSC')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "72c937e6-0a79-4762-a786-2bbb74e32f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagnoal\n",
    "x = [0, 1]\n",
    "y = [0, 1]\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "# Plot multiple lines\n",
    "plt.plot(fprs2, tprs2, label=f'First 2 min, AUC: {roc_aucs2:.2f}')\n",
    "plt.plot(fprs3, tprs3, label=f'First 3 min, AUC: {roc_aucs3:.2f}')\n",
    "plt.plot(fprs5, tprs5, label=f'First 5 min, AUC: {roc_aucs5:.2f}')\n",
    "plt.plot(x, y, color='black', linestyle='--')\n",
    "\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('ROC, Survival')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1098b13a-cb70-491a-878f-27d03d865b0f",
   "metadata": {},
   "source": [
    "## Plot, start at min 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7446d5aa-f661-4605-b4e1-89532fa3d9d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagnoal\n",
    "x = [0, 1]\n",
    "y = [0, 1]\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "# Plot multiple lines\n",
    "plt.plot(fprr21, tprr21, label=f'First 2 min, AUC: {roc_aucr21:.2f}')\n",
    "plt.plot(fprr31, tprr31, label=f'First 3 min, AUC: {roc_aucr31:.2f}')\n",
    "plt.plot(fprr51, tprr51, label=f'First 5 min, AUC: {roc_aucr51:.2f}')\n",
    "plt.plot(x, y, color='black', linestyle='--')\n",
    "\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('ROC, ROSC, start at min 1')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "3795cf6d-1f6e-4239-a3d7-171f22407da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagnoal\n",
    "x = [0, 1]\n",
    "y = [0, 1]\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "# Plot multiple lines\n",
    "plt.plot(fprs21, tprs21, label=f'First 2 min, AUC: {roc_aucs21:.2f}')\n",
    "plt.plot(fprs31, tprs31, label=f'First 3 min, AUC: {roc_aucs31:.2f}')\n",
    "plt.plot(fprs51, tprs51, label=f'First 5 min, AUC: {roc_aucs51:.2f}')\n",
    "plt.plot(x, y, color='black', linestyle='--')\n",
    "\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('ROC, Survival, start at min 1')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a7efb1-b26a-495c-a08b-f2cfc0421c4c",
   "metadata": {},
   "source": [
    "## Median from 17-25 min, relationship to ROSC and Survival with Favorable Neurological Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "359d153c-fc00-47bd-9fb9-ebb36847aec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['Medians17-25min']) == False][['Medians17-25min', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['Medians17-25min']\n",
    "y_true = dfstart['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocrosc = pd.DataFrame(fpr, columns=['fpr, ROSC'])\n",
    "rocrosc['tpr, ROSC'] = tpr\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['Medians17-25min']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fpr, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tpr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005089ea-824b-49be-a2fc-06db772c79f9",
   "metadata": {},
   "source": [
    "# Median post-ROSC, predicts survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6eb18978-74d3-499d-b6db-5ce69a47b626",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import statsmodels.api as sm\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(5, 4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5e31586c-fdbf-463b-9fbf-12df76b873bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['ROSC-1min Median']) == False][['ROSC-1min Median', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['ROSC-1min Median']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprp1, tprp1, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucp1 = auc(fprp1, tprp1)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprp1, tprp1, color='blue', lw=2, label=f'ROC curve (area = {roc_aucp1:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprp1, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "824c7952-ba57-42cf-90d5-716f035ae28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROSC-1min Median</th>\n",
       "      <th>ROSC</th>\n",
       "      <th>CPC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>41.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ROSC-1min Median  ROSC  CPC12\n",
       "8               59.0     1      1\n",
       "24              41.5     1      1\n",
       "44              63.0     1      1\n",
       "45              64.0     1      1\n",
       "55              70.0     1      1\n",
       "60              71.0     1      1\n",
       "65              60.0     1      1"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfstart[dfstart['CPC12'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "6e818402-9ca2-478e-b716-a167d6ba102d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROSC-1min Median</th>\n",
       "      <th>ROSC</th>\n",
       "      <th>CPC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>77.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>72.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ROSC-1min Median  ROSC  CPC12\n",
       "5               66.0     1      0\n",
       "15              64.0     1      0\n",
       "17              77.0     1      0\n",
       "46              68.0     1      0\n",
       "69              86.0     1      0\n",
       "74              72.0     1      0\n",
       "79              75.0     1      0\n",
       "85              79.0     1      0\n",
       "90              68.0     1      0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfstart[(dfstart['CPC12'] == 0) & (dfstart['ROSC-1min Median'] >= 63)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "c3f4bf42-535c-4758-8457-04b180dfd745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['ROSC-2min Median']) == False][['ROSC-2min Median', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['ROSC-2min Median']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprp2, tprp2, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucp2 = auc(fprp2, tprp2)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprp2, tprp2, color='blue', lw=2, label=f'ROC curve (area = {roc_aucp2:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprp2, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2f5ff71f-c1d5-49e2-b46b-accbc7058e4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['ROSC-3min Median']) == False][['ROSC-3min Median', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['ROSC-3min Median']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprp3, tprp3, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucp3 = auc(fprp3, tprp3)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprp3, tprp3, color='blue', lw=2, label=f'ROC curve (area = {roc_aucp3:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprp3, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprp3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c90f3c9c-b285-434e-ab17-788550ecb21e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['ROSC-4min Median']) == False][['ROSC-4min Median', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['ROSC-4min Median']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprp4, tprp4, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucp4 = auc(fprp4, tprp4)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprp4, tprp4, color='blue', lw=2, label=f'ROC curve (area = {roc_aucp4:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprp4, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprp4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "5ffe5022-173f-41b9-baa4-e828c42f8ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfstart = df[np.isnan(df['ROSC-5min Median']) == False][['ROSC-5min Median', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfstart['ROSC-5min Median']\n",
    "y_true = dfstart['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fprp5, tprp5, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_aucp5 = auc(fprp5, tprp5)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fprp5, tprp5, color='blue', lw=2, label=f'ROC curve (area = {roc_aucp5:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Survival, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "#Put into a dataframe\n",
    "rocsurv = pd.DataFrame(fprp5, columns=['fpr, Survival'])\n",
    "rocsurv['tpr, Survival'] = tprp5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee31c094-8d8a-4f45-8708-e7a413c9cf2e",
   "metadata": {},
   "source": [
    "## Plot on the same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "6cff3644-af9d-49f0-8bfb-c5b43e5c1071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagnoal\n",
    "x = [0, 1]\n",
    "y = [0, 1]\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "# Plot multiple lines\n",
    "plt.plot(fprp1, tprp1, label=f'First 1 min, AUC: {roc_aucp1:.2f}')\n",
    "plt.plot(fprp2, tprp2, label=f'First 2 min, AUC: {roc_aucp2:.2f}')\n",
    "plt.plot(fprp3, tprp3, label=f'First 3 min, AUC: {roc_aucp3:.2f}')\n",
    "plt.plot(fprp4, tprp4, label=f'First 4 min, AUC: {roc_aucp5:.2f}')\n",
    "plt.plot(fprp5, tprp5, label=f'First 5 min, AUC: {roc_aucp5:.2f}')\n",
    "plt.plot(x, y, color='black', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('1-Specificity')\n",
    "plt.ylabel('Sensitivity')\n",
    "plt.title('ROC, Post-ROSC Signal Predicts Survival')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c1b539-6e9c-460c-aac3-54f6022cf168",
   "metadata": {},
   "source": [
    "# Highest BOx val pre-ROSC (by 2 min) relationship to outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa95cd34-f532-4933-a517-e89732ead317",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import statsmodels.api as sm\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(5, 4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8e4c91b-7506-4168-8d96-baf183e99e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Highest BOx Val</th>\n",
       "      <th>ROSC</th>\n",
       "      <th>CPC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Highest BOx Val  ROSC  CPC12\n",
       "0              18.0     0      0\n",
       "1              99.0     0      0\n",
       "2              50.0     0      0\n",
       "3              24.0     0      0\n",
       "4              50.0     0      0\n",
       "..              ...   ...    ...\n",
       "88             30.0     0      0\n",
       "89             21.0     0      0\n",
       "90             65.0     1      0\n",
       "91             17.0     0      0\n",
       "92             46.0     0      0\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfhigh = df[np.isnan(df['Highest BOx Val']) == False][['Highest BOx Val', 'ROSC', 'CPC12']].copy()\n",
    "dfhigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "856cda6f-4cfc-472b-9a03-bc5dd962dffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ukkk33X+3XUrvns7tdkw6nVOuri+ppNysJ2WWM1Ju1pMyy77Ll0FRjPly7yKZdP9doy1durT5eGxsLJCxBmyt2NgkjEYZcJBdarUdOp2TlJsVpMxyRsrNelJm2XP9+jXi4+OpX78hBoMjJlPe13KhiCbd9L7cq1evWkwzCg0NRaVSZejrtZbRaJJRfjkg5WY9KbOckXKznpRZ5kwmE8ePH+HEiWPY2dnh5eWNolTJt+crko38lStXpnr16mzZssXi+KZNm/Dz88PLy8tGkQkhhCgqEhMT2LhxNSdOHAOgbt2GeHrmb/6weU03KSmJ/fv3A3D79m3i4+PZtm0bAC1btsTLy4t3332XdevWceHCBfN1r776KmPHjsXHx4fWrVuze/duDh8+zMKFC23yOoQQQhQdt2/fYMeOLSQlJaLR2NOhQxdq1qyT789r86QbERHBa6+9ZnEs/efvv/+eVq1aYTKZMBotO7W7d+9OcnIy8+bNY9GiRVSpUoUvv/ySwMDAAotdCCFE0XPy5O8cO3YYRVHw8ipFcPAT+V7DTWfzFakKI1m5xTqy4o31pMxyRsrNelJmGZ06dZwjRw5Sp0592rbtiL29vcXjISFO/PyzhlwOD8qUzWu6QgghRH4zGo2o1Wkjkhs3bk6pUt74+FQt8DiK5EAqIYQQIjsUReHUqeP8+uuPpKamAmlrOdgi4YLUdIUQQhRTyclJ7NmznX/+SVsa+O+/L9CgQSObxiRJVwghRLFz714YO3ZsJi4uFrVaTWBgB+rVa2jrsCTpCiGEKD4UReHs2VP89tsBTCYTOp07wcFP4O1dxtahAZJ0hRBCFCMnThzj999/A6B69Zp06NAVBwcHG0f1kCRdIYQQxUbdug04f/4MTZq0oGHDxlZvgBMfD+Hh+TfGWJKuEEKIIktRFO7evUP58hUBcHFxZfDgEWg09o+5MqNLl+wYMcKRq1fzL+nKlCEhhBBFUmqqnl27trJ27S+Ehl4yH89Jwl2/XkPXrs5cvpw2l1elyp91o6SmK4QQosiJiAhn+/ZNREdHolKpSExMyNF99HqYNMmB777Tmo/Vq2ekQgUVkLu92TMjSVcIIUSRcvHieQ4c2I3BYMDFxZWuXXuam5etERamYtQoJ44ff7h37sCBqUyfnoxG45KXIZtJ0hVCCFEkpKamcvDgHi5ePA9A5cpV6Ny5O05Ozlbf69AhNc8952geNKXVKnzySQrPPJOKlWOvrCJJVwghRL4x5eEeC7dv3+LixfOoVCqaNw+gadNWqFQqq55DUeCbb7R88okWkyktu1aqZGLRoiSaNMn/DSEk6QohhMhzSUnw8ccO/PyzPcnJeVV19KNduyhu3vTho4+q5ckd27c38O23yZQqVTAb7knSFUIIkaf++UfFyJFOnD2rfvzJWdBoDHTosIejR/2Ji9MBcOBAUF6ECMCbb6Ywbpwede7CtIokXSGEEHlm5041L73kRExMWu3WyUmhQQPrm22dnSNp2nQN7u53qVPnNkePDiWvRhPrdAqjR+vp2NGYJ/ezhiRdIYQQuWY0wuefa5k58+GSi9Wrm1i8OIl69axLuqGhl9m7dzt6vR5HRyeGDWvOBx8k5XXINiFJVwghRK5ERKh48UVH9u17mFJ69Ehl9uxkdLrs38doNHLkyAHOnDkFQLlyFejatSeurm55HbLNSNIVQgiRYydP2jFypBO3b6dNvbGzU3j//RTGjLFu6k1CQjxbt27g/v27ADRp0pyWLdugLsgO1wIgSVcIIYTVFAWWLbPn/fcd0OvTsmvp0iYWLEimTRvr+0q1WgcMhlQcHBzo1KkbVav65nXIhYIkXSGEEFZ7+20Hli17uHRiy5YGFi5Mply57E+9MZlMqFQqVCoV9vb2dOv2JHZ2duh07vkRcqEgGx4IIYSwyv37KouE+/zzetauTbIq4cbHx7F+/SpOnjxuPubh4VmsEy5ITVcIIYSVUlIe/rtbt1QmT0559MmZuHHjH3bv3kpSUhKRkeE0aOCHg4NjHkdZOEnSFUIIkWOOVuRKk8nEH38c5Y8/jgJQurQ3wcEhJSbhgiRdIYQQBSAxMYGdO7dw+/ZNAOrV8yMwsD0aTclKQyXr1QohhChwRqOB1at/Ji4uFo3GnvbtO1OrVl1bh2UTknSFEELkK7VaQ5MmzTl37k+Cg5/A09PL1iHZjCRdIYQQeS4pKYmkpES8vEoBUL9+I+rUaVDimpP/q2S/eiGEEHkuLOwOO3ZsQq1WM2DAYBwcHFGpVCU+4YIkXSGEEHlEURT+/PMER48ewmQy4eHhSVJSUokanfw4knSFEELkWnJyMnv3bufatVAAatSoTfv2XdBqtY+5smSRpCuEECJX7t+/y/btm4iLi8XOTk1gYHvq1/dDZc2OByWEJF0hhBC5cuLEMeLiYtHp3AkODsHbu6ytQyq0JOkKIYTIlfbtu+LsfBh//7Y4ODg8/oISTDY8EEIIYZWYmPu0b7/X/LOTkxNBQZ0l4WaD1HSFEEJki6Io/PXXWQ4d2kv79kYiIkoDdWwdVpEiSVcIIcRjpabq2b9/N5cu/QXApUs1uXLFF9/iudd8vpGkK4QQIkuRkeFs376JqKhIVCoVdeoEMnFiBxRFBaTaOrwiRZKuEEKIR7py5W/27NmOwWDAxcWFLl16YjRW/v+EK6wlSVcIIcQjabUOGAwGKlXyoXPnHjg7O3Pzpq2jKrok6QohhLBgNBpRq9UA+PhUpVev/pQvXwk7O5nwkltSgkIIIcwuX/6bH39cTExMtPlYxYo+knDziJSiEEIIjEYDBw7sZufOzcTHx3HmzElbh1QsSfOyEEKUcDEx0ezYsZkHD+4B0KxZK1q0CLBxVMWTJF0hhCjBrl69wp4929HrU3B0dKRTp+5UqVLN1mEVW5J0hRCihLp69TLbtm0EoGzZ8nTtGoKbm5uNoyreCkXSvXbtGlOmTOHEiRM4OTnRs2dPxo0bh6Nj1hsfJyYm8s0337Bt2zYePHhA2bJleeKJJ3j++edlD0chhHiMKlWqUaZMWcqXr4S/f6B5xLLIPzZPurGxsQwbNowKFSowe/ZsIiMjmTp1KtHR0cyYMSPLaz/++GN27drF2LFjqVmzJmfOnGH27NnExMTw/vvvF9ArEEKIoiMs7DZly5bHzs4OtVpD795PodHYPBWUGDYv6RUrVhAbG8u6devw8vICQK1WM27cOF588UV8H7Gwp8FgYNu2bYwaNYqhQ4cC4O/vz507d9iyZYskXSGE+BeTycSRIwc5deo4zZq1olWrNgCScAuYzacMHThwgICAAHPCBQgODkar1bJ///5HXqcoCkajMUP/g06nQ1GUfItXCCGKmri4ONasWcmpU8cB0OtT5O+kjdj8K05oaCj9+vWzOKbVavHx8SE0NPSR19nb29O3b1+WL19O06ZNqVGjBmfPnmXlypUMGTIkv8MWQogi4caN6+zYsZnExETs7bV06NCFGjVq2zqsEsvmSTc2NhadTpfhuE6nIyYmJstrP/74Yz766CMGDhxoPjZ06FBefvnlXMWkVtu8AaBISS8vKbfskzLLGSm37DOZTPz++xF+//0IAN7eZeje/Qk8PDxzfW+N5uFmByqVCo2m+P0+VPm0n4PNk+6jKIqC6jGvesaMGezbt4/JkydTrVo1zp8/z+zZs9HpdLz66qs5fm6dzinH15ZkUm7WkzLLGSm3x4uMjOTkybTm5KZNm9KtWzfs7e3z5N6xsQ//rdVq8PQstKmk0LF5Sel0OmL//Rv8f3FxcY8cRAVw6dIlFi9ezDfffEOnTp0AaNGiBSqViunTpzN48GBKlSqVo5hiY5MwGk05urYkUqvt0OmcpNysIGWWM1Ju2adSOdCxY1dUKhX+/s2JjU0iPl6fJ/eOiVEBzgDo9QaiolLy5L6Fibu7U76sN23zpOvr65uh71av13Pjxo0Mfb3/duXKFQDq1q1rcbxu3boYDAZu376d46RrNJowGOQDbS0pN+tJmeWMlFtGiqJw6tRxypevSPnyFQGoUaOOuek3L8vMYHjYCqkoSrH8XeTXODObJ9127drx7bffEhUVhadnWl/Dzp070ev1BAUFPfK6ihXT3lTnz5+nQoUK5uPnzp0DoFKlSvkYtRBCFKzoaMtk928pKUn8/vtWwsKu4eTkSrduw7G3dwBAowGDIevrrRUVJRvY55TNk+6gQYP44YcfeOmll3jppZeIiIjgs88+44knnrBoXn733XdZt24dFy5cAKBBgwb4+fnx0UcfER4eTrVq1Th79izffPMNPXr0sJiCJIQQRZXJBMOHO7JtW+b9sZUq3WLAgFW4u8eSmqph/foOjB/vBfw3Mbrke6zi8WyedHU6HcuWLWPKlCm88sorODo6EhISwrhx4yzOM5lMGI1G889qtZp58+Yxa9YsFixYQHh4OOXLl2fIkCG88MILBf0yhBAiXyxZYv+IhKvg73+ULl12oVabiIjwYuXKAdy7V65A4ytbVub7WkOlyAzpDKKiEoplH0V+0Wjs8PR0kXKzgpRZzpS0crt1S0Xbti4kJKTVWjt3NqBWg0ploGbNdZQq9TcA4eH1uHq1B0ajQ4Z7qFRgb68hNdWQ5/2U5cubGDtWT/nyxS+NeHm55MvUNJvXdIUQQmSkKPD2247mhDt0qJ4vvkj5/8cUtm838c8/agIDg6hfvxEqlQlIynCftC8qGqKiUkrEF5XCLkdpPDQ0lDfeeIPAwEAaNGjA+fPnAZgzZw5Hjx7N0wCFEKIkWrNGw65dafWismVNfPBBMkajAUhbkKJDh6706zeIBg0aP3ZNA1F4WJ10//rrL/r378/vv/9Oy5YtLfpZExISWLFiRZ4GKIQQJU1EhIr333/YVDx1aiy//76FXbu2mddMdnBwwNu7rK1CFDlkdfPyjBkzqF27NkuWLMHe3p4tW7aYH/Pz82PHjh15GqAQQpQ077/vQEREWp1o4MDbxMauJjo6Cjs7OyIiwild2tvGEYqcsjrpnjx5ks8//xwnJyeLWi5A6dKlCQ8Pz7PghBCipNm9W83q1faAQps2p2jQYCvR0QZcXFwJDg6RhFvE5Wgg1aPW74yJiUGr1eYqICGEKKni42HcOEfs7fX07LmFxo3/xGQCH5+qdOrUHScnWXO6qLO6T7d27drs2rUr08cOHjxI/fr1cx2UEEKURJ984sDt23YMGvQLjRv/iUqlolWrQHr27CMJt5iwuqb7zDPP8Oabb+Lk5ESvXr0ACAsL4+jRo6xevZrZs2fneZBCCFHc/f67HYsXp7UiHjvWlvr1HxAc3I2KFSvbODKRl6xOuj169ODGjRvMmTOH5cuXA/DKK6+gVqt59dVX6dixY54HKYQQxVlCQiqffhqHolQBYMiQcgwbNgK1WpZSKG5y9Bt94YUX6N27NwcPHiQiIgJPT08CAwPNmxAIIYTInujoKJYv30z79lFcuvQcPj6ejB6dKgm3mLL6t3r8+HHq1atHuXLlGDBggMVjCQkJXLhwgRYtWuRZgEIIUVxduXKJ3bt3oFbrSU52xs0tnpkznVCrbR2ZyC9WD6R65plnMux/m+7atWs888wzuQ5KCCGKM4PBwKZNe9ixYxNGo57r132YN+95+vQpT716slRjcWZ1TTer/REMBgN2dnm/QLQQQhR1d+6oOHRIzbFj8eh0a/H2DgPg4ME27N3bkRo1FMaOTbRxlCK/ZSvpxsfHExsba/75wYMH3Llzx+Kc5ORk1q5dS+nSpfM2QiGEKIIiIlQcPqzm4EE1hw5pCA1Nq5C0b3+U9u3DSEpyZM2aPly+XIuKFU18+20yDhk3CRLFTLaS7tKlS5k7dy6QttD2yy+/nOl5iqLw/PPP5110QghRRMTFwZEjag4e1HDokJrz5zPvmD1wIAg3tyRSUloxcKArgYEJNGpkQiPjpkqEbP2a27Rpg7OzM4qi8PnnnzNkyBAqVKhgcY5Wq6VWrVq0bNkyXwIVQojCJCkJjh9Xc+hQWqI9fdoOozHjbj86XQzt2h0iPLwzbdpA27ZGmjVr+/+1Wn2Bxy1sK1tJt0mTJjRp0gSApKQkBgwYQNmysruFEKLkSE2FU6fsOHRIw8GDao4fV6PXZ76lnkql4OdnokOHS5QqtRGjMYkmTSAgoF0BRy0KG6sbNB7VtCyEEMWJyQTnz9tx4EBan+yRI2oSEx+9b23t2kYCA420bWvE31/P5ctHOHHiGEYjlC5dhnr1/AowelFY5agXwWg0cuDAAUJDQ0lOTrZ4TKVSMWbMmDwJTgghssNohHnz7Ll0KW8muEZFwbFjGqKiHp1kq1Qx0batgcBAI23aGClbNm1mR0JCPDt3buHOnVsA1K/fiDZtgtBIp60gB0k3KiqKwYMHc/XqVVQqlXkKkUr18M0pSVcIUZDmzNHyySf5O/S3bFkTgYFG2rUz0KaNER+fjNMnw8LusG3bBpKSErG3t6d9+y7UrFknX+MSRYvVSffLL7/EwcGBvXv30qFDB1auXImHhwc///wz+/btY+nSpfkQphBCZO7KFRUzZuT9lqKengpt2hjMTcY1aphQPbriC4CzsxMGgwEvr9IEB4fg6emV53GJos3qpHv06FHGjBlDmTJlALCzs8PHx4fx48ej1+uZNm0aM2fOzPNAhRDiv0wmeOMNR1JS0rLhyJF6RoxIzfV9NRqFqlUVsrPWj8FgMDcdu7t78uST/fDyKv3IfcdFyWZ10r179y4VK1ZErVZjZ2dHUlKS+bEOHTrw5ptv5mmAQgjxKMuW2XP0aNqfsapVTXzwQQrOzgX3/Hfu3GLXrq106NCVypXTdggqW7Z8wQUgihyr12z09PQkPj4egDJlynDp0iXzYzExMRiNxryLTgghHuHOHRWTJz/sx/3ii+QCS7iKonDq1HHWr19FfHwcJ04czXKJXCHSWV3TrV+/PpcvX6Z9+/a0a9eOb775BldXV+zt7Zk5cyaNGjXKjziFEMJMUeDttx2Jj09rVh48WE/btgXzhT85OYndu7dz/fpVAGrWrEP79p0tBpMK8ShWJ90hQ4Zw48YNAF5//XX+/PNPxo8fD4CPjw/vvfde3kYohBD/sWaNmh070v58lSlj4qOPUgrkee/dC2P79k3Ex8ehVqsJDOxAvXoNJeGKbFMpuWwTURSFS5cuoVKpqF69erGYixYVlYDBINtrZZdGY4enp4uUmxWkzHJGo7HDZHKhTh2F8PC0RLdkSRI9exry/bkjIyNYuXI5JpMJd3cPunYNwdu7TL4/b27Jey1nvLxcUKvzfte8XGdIlUpF7dq1gbQEvH79enr16pXrwIQQIjNjx2JOuCEhqQWScAE8Pb2oUaM2RqOB9u274iBbAokcyLNq6ZYtW5gzZw7Xrl2TpCuEyBe7dqlZvjzt3+7uClOn5m+z8oMH93Fzc8PR0QmVSkWHDl2xs7OT5mSRY9muO3/33Xd07NiRRo0a0bt3bw4cOADAyZMn6dWrF2+++SaxsbF88MEH+RasEKLkio+HN954uAjGxInJ5qUX85qiKJw79ydr1vzM7t3bzSOT1Wq1JFyRK9mq6f7444/MnDkTNzc3atWqRVhYGGPGjOGDDz5g0qRJaDQaxowZw7PPPotzQU6SE0KUGFOnOnDrVlo9ISjIyNNP50+zsl6vZ//+nVy+/DcAKlXaAhiy2IXIC9lKuqtXr6ZZs2bMnz8fV1dXjEYjH3/8MR999BEVK1Zk0aJFVKlSJb9jFUKUUMeP27FwYVrSc3KCL79MeeySjDkREfGA7ds3ER0dhUqlwt+/LY0bN5Parcgz2Uq6165d4/PPP8fV1RVIa2J58cUXWbVqFa+99pokXCGKCUWBCxfsuHEj70dt5sann2pRlLTEN3kyVK2qYMjjiu7Fi+c5cGA3BoMBFxdXunbtSfnyFfP2SUSJl62km5SUZF5rOV36JvaScIUoHpKSYMIER37+ufA2ozZpYuS119TExeXtfVNTUzl+/AgGg4HKlavQuXN3nJykq0zkvVyPXi4O83KFKOn++UfFs886ce5c3uxHmx/s7RVmz05Bo8n7ZGhvb0/XriHcvHmdZs1aSnOyyDfZzpjTpk3Dzc0tw/FPP/3U3OwMafN2v/3227yJTgiR73bsUDNmjBMxMWmJxtlZ4bnn9PzrY10otG1roH79vLvfpUt/YTKZqFMn7aZly5ajbNlyefcEQmQiW0m3QoUKhIWFERYWluH4nTt3LI7JN0QhigajEaZP1/Lllw8XefD1NbF4cRJ16xbWlYty39dsMBg4fHgf58+fQa1WU7Zsedn3VhSYbCXdPXv25HccQogCFB6u4oUXHDlw4OGfgJ49U5k9O5lMGrSKjZiYaLZv30R4+H0AmjRpjru7h22DEiWKdMgKUcKcOGHHqFFO3L6dVmtUqxXefz+Fl15KzZdpOIVFaOhl9u7djl6vx9HRic6du+PjU9XWYYkSRpKuECWEosCSJfZ88IEDqalp2dXb28SCBcm0bl2898E+fHg/f/55AoBy5SrQtWtPXF2LcZVeFFqSdIUoIebOtWfSJEfzz61aGViwIJly5Yr/5uvpmxM0adKcli3boFYX3lHaoniTpCtECbF378OP+/PP6/nwwxSK88qGBoPBPKWxWbNWVKxYWRa7EDZXuJadEUIUiHfeKb4J12g0cuTIAVav/hmDIRVIm1UhCVcUBlLTFUIUG/HxcezcuYWwsNsAXLsWSs2adWwclRAP5TjpxsXFcfr0aaKioggKCsLd3T0v4xJCCKvcuPEPu3ZtJTk5Ca1WS4cOXfH1rWXrsISwkKOkO3fuXBYsWEBycjIqlYpff/0Vd3d3hg0bRps2bXjuueesut+1a9eYMmUKJ06cwMnJiZ49ezJu3DgcHR0fe210dDRfffUVu3btIiYmhgoVKjBixAgGDRqUk5cmhChiTCYTf/xxhD/+OAZA6dLeBAeH4O7uaePIhMjI6qT7448/MnfuXP73v//Rtm1bnn/+efNjHTp0YMeOHVYl3djYWIYNG0aFChWYPXs2kZGRTJ06lejoaGbMmJHltQkJCQwdOhQHBwfeffddSpUqxfXr10lNTbX2ZQkhiqgjRw6apwPVq+dHYGB7WRNeFFo5SrrDhw/n7bffxmi0nNtXpUoVrl+/btX9VqxYQWxsLOvWrcPLK20pNrVazbhx43jxxRfx9fV95LXz588nOTmZVatWmWvFrVq1svIVCSGKMj+/ply9eplWrdpQq1ZdW4cjRJasHr188+ZN2rZtm+ljLi4uxMbGWnW/AwcOEBAQYE64AMHBwWi1Wvbv35/ltatXr6Z///7ZaoYWQhQPiqJw8+YN889ubm78738jJOGKIsHqpOvm5kZ4eHimj92+fZtSpUpZdb/Q0NAMtVmtVouPjw+hoaGPvO7mzZuEh4ej0+l4/vnnadCgAa1atWLixIkkJydbFYMQomhISkrkp59+Yu3alVy79vDvgyx2IYoKq5uXAwICWLhwIZ06dTKv8qJSqTAYDPz8888EBgZadb/Y2Fh0Ol2G4zqdjpiYmEdel574p0+fTrdu3ViwYAFXrlxh5syZpKamMmXKFKvi+De1WqYvWyO9vKTcss8WZfbvHcA0GjuKWrdnWNhttm7dRHx8HGq1BqMxFY1G3nOPI5/PnMmvdcit/ti9+uqr9O/fn549e9K5c2dUKhU//PADf/31F3fu3OGrr77Kk8AURclym0CTKW3rMV9fX6ZOnQqkfSEwGAxMnz6d1157DW9v7xw9t07nlKPrSjopN+sVZJn9O8l6errgnPd7wecLRVE4cuQIu3fvxmQyUapUKQYMGEDZsmVtHVqRIp/PwsHqpFulShV+/vlnpk6dys8//4yiKKxfv55WrVoxY8YMKlSoYNX9dDpdpv3AcXFxWQ6i8vDwAMDf39/iuL+/PyaTidDQ0Bwn3djYJIzGwrqfaOGjVtuh0zlJuVnBFmVmMDgCac2wUVEJpKQUyNPmSnJyMrt2bePq1SsA1K5dlz59epGSYiIqKsHG0RUN8vnMGXd3J+zs8r51IEcNTDVq1GDRokXo9XqioqJwd3fP8WAmX1/fDH23er2eGzdu0K9fv0deV7lyZewzWcdOUdIWb89NYRmNJgwGeXNaS8rNegVZZumfDQCDwYTBUCBPmyvXr1/n6tUr2Nmpadu2A35+jXBwcCAxMUHea1aSz6d1lHzaB8TqpLt3716CgoKws7NDq9XmuomnXbt2fPvtt0RFReHpmTaZfefOnej1eoKCgh55nVarpU2bNhw5csTi+JEjR9BoNNSoUSNXcQlRWEydqmXjRg2KkrtOpjt3it5mub6+NWnZsjVVqlTD27tsll1OQhQFKkWxLp/XqVOH0qVL06tXL/r27ZtlE3B2xMbGEhISQsWKFXnppZeIiIjgs88+IzAw0GJxjHfffZd169Zx4cIF87EzZ87wv//9jx49evDkk09y5coVvvrqKwYOHMi7776b45iiouRbtDU0Gjs8PV2k3KyQ3TL75x8VLVu65ulza7UKV6/Go9Xm6W3zhF6fwpEjB2nRIgBnZ5cMj8t7zXpSZjnj5eWSL4PPrK7pzp8/n7Vr17J8+XIWL16Mn58f/fr1o0ePHri6Wv/HQafTsWzZMqZMmcIrr7yCo6MjISEhjBs3zuI8k8mUYTEOPz8/5s+fzxdffMELL7yAh4cHQ4YM4bXXXrM6DiEKo7i4hzU7rVbJ9eAne3uF555LLZQJNzz8Ptu3byImJpq4uFhCQvraOiQh8pzVNd10sbGxbNy4kXXr1nH27FkcHR3p0qULffv2JSAgIK/jLFDyjdA68k3aetkts7Nn7ejUKa3GN3y4nunTi8DoJyspisJff53l4MG9GI1GXF3d6Nq1J+XKZRyUKe8160mZ5Uyhqemm0+l0DB48mMGDBxMaGsrq1atZt24dmzdvtmgCFkKIR0lN1bN//24uXfoLgCpVqtGpUzccHWV6iyiecj09XlEUwsLCuHv3LvHx8eSw4iyEKGFiY2PYvHktUVGRqFQq/P0Dady4uQyWEsVajpPu9evXWbNmDevXr+fevXuUKVOGESNG0Lev9MMIIR7P0dEJRVFwcXGha9cQypevaOuQhMh3Vifd1atXs2bNGk6ePIm9vT0dO3akb9++BAYG5stEYiGKq+Rk+PBDe65ceXTNLiameNX6DAYDarUalUqFVqulR4/eaLUOOBeV5bGEyCWrk+57771HvXr1eO+993jiiSdwd3fPj7iEKPbeeQfmzMn+MOJM1oIpUqKiItm+fRN169anUaNmAHh4yEbzomSxOumuW7eOOnXq5EcsQpQYf/xhx6xZ2T/f01OhV6/U/Ason12+/Df79u0gNTWV06dPUK+eX6YryglR3FmddCXhCpE7ej289pqDeZm5CRNSGDo064Sq0yn8/6ZeRYrRaODw4f2cO/cnABUqVKJLlx6ScEWJla2kO2fOHPOuHnPmzMnyXJVKxZgxY/IkOCGKo9mztfz1V9r4h0aNjLz6qr7IbbOXHTEx0ezYsZkHD+4B0KxZK1q0CJCxH6JEy9biGHXq1GHlypX4+fk9tqarUqn466+/8ixAW5BJ5NaRyffZ9/ffdnTs6Exqqgq1GvbsSaJu3SKw84CV9Ho9P/64iKSkJBwdHenUqTtVqlTL9X3lvWY9KbOcseniGBcvXsz030KI7DMaYexYR1JT00Ykv/02NGxYNHb7sZZWq6Vp05ZcuXKJrl1DcHNzs3VIQhQKxbBRS4jCackSe/74I20/2xo1THz4oR1JSTYOKg/FxcWSmpqKl1cpAPz8mtKgQWPUarWNIxOi8LC67ly3bl3OnDmT6WPnzp2jbt26uQ5KiOLm5k0VU6Y8HAn11Vcp5HAL6kLp+vWrrFz5A9u2bUCv1wNpXU2ScIWwZHVNN6suYJPJJEu4CfEfigJvveVIYmLaZ2PYMD2tWxePvjWTycSxY4c5deo4ADqdO3p9CtrCuI2REIVAnjYvnz9/XvpuhPiPX3/VsGdP2ketfHkTH36YQg4amQqdhIR4du7czJ07twFo2LAxrVu3Q62WXishHiVbn45ly5bx/fffAw+nBP33m2xKSgoREREEBwfnfZRCFFHh4So++OBhs/L06ckUh++lN29eZ9euLSQlJWFvr6VDh67UqFHL1mEJUehlK+mWKlWKmjVrAnD79m0qV66MTqezOEer1VKrVi2eeeaZvI9SiCLq/fcdiIxMq9X27p1KcLDRxhHlnqIonDp1nKSkJEqV8iY4OESWcxQim6zexH7o0KF8/PHH+Pr65ldMNifz2awj8wAzt2OHmiFD0hby9/RUOHQoAW/vtI9bUS+zxMQETp/+g5YtW6PRFNzqUkW93GxByixnCs0m9suXL8/zIIQoCJcv23H8eMH0pSqKis8/f9gFM2lSsjnhFkV37tzi1q3rtGzZBgBnZxdatw6ycVRCFD3ZSrp37tzB29sbe3t77ty589jzK1SokOvAhMhL16+rCAx0RlEKfnR9+/YGBg4smitgpDclHzt2GEVRKF26LNWr17B1WEIUWdlKup06deKXX37Bz8+Pjh07PnZaUFFfBlIUP2fOqG2ScN3cFGbMSKYozqRLTk5i165t3LhxDYBatepSuXIVG0clRNGWraT76aefUrlyZfO/ZS6uKMpCQlLp0CH/BzTZ2UFAgAEfn6LXrHz37h127NhMfHwcarWatm07UrduA/nsC5FL2Uq6ffr0Mf+7b9+++RaMEAWhWTPjY7fSK8nOnfuTQ4f2YjKZcHf3IDj4CUqX9rZ1WEIUC3kyiz0lJYVbt25RtWpVWfZNiCLOxcUVk8lEjRq1ad++M1ptEdzIV4hCKkejl2NjY8175p47d45Ro0YRExNDxYoVWb58OeXLl8/zQIUQ+cdgSDVP/alWzZc+fQZRrlx5aU4WIo9ZPX9i1apVFgtjzJgxA3d3d9555x0UReHbb7/N0wCFyI7bt1X8/rvdI/+7cqXoL7uYHxRF4ezZ0/z442Li4uLMx8uXryAJV4h8YHVNNywsjOrVqwMQHx/PH3/8wcyZM+natSs6nY7Zs2fneZBCPIpeD5MmOfDdd7LAvrX0+hT27t1JaOglAC5cOEOrVm1sHJUQxZvVSVev16PRpF12+vRpTCYTrVu3BqBSpUqEh4fnbYRCPEJYmIpRo5w4fty6cQTVqxe90cR5LTz8Adu3byQmJho7OzsCAtri59fU1mEJUexZnXTLly/PH3/8QatWrdi9ezd16tTB1dUVgMjISPO/hchPhw6pee45R8LD05qNtVqFp59Oxckp6+vq1TPStWvRXKgiLyiKwl9/nePgwT0YjUZcXd3o2rUn5crJgjZCFASrk+6TTz7J3Llz2b17NxcvXuTtt982P3bu3DmqVq2al/EJYUFRYM4cLZ98osVkSutzrFjRxKJFSTRtKuvKPs7ff19g376dAPj4VKNz5244Oj7mm4oQIs9YnXRffPFFNBoNJ0+epHPnzgwdOtT82KVLl+jatWueBihEuthYeOUVR7ZufbjAflCQgXnzkilVSpqMs6NGjVqcPXsKX99aNGnSQgZLCVHArN5lqCSQ3TisUxC7mJw/b8ezzzpx7drDUchvvJHCW2/pKYpTwwty55cbN/6hcuUq5gRrNBqL7Hx62THHelJmOVNodhlKFx8fz+nTp4mOjsbT05NGjRpJf67IF6tWaRg3zpGkpLSk4eGhMHduEl26FP29afOTwZDKoUP7uHDhLC1btqZ5c3+AIptwhSgOcpR0Fy1axJw5c0hOTkZRFFQqFY6Ojrz66quMGDEir2MUJVRKCnzwgQNLlz6cDtSwoZHFi5OoUkUaaLISHR3F9u2biIh4YOtQhBD/YnXSXbduHZ9//jnt2rWjT58+lClThvv377Nu3TqmT5+Op6cnvXv3zodQRUly61badKCTJx/WygYP1jN1agqOjjYMrAi4cuUSe/fuIDVVj5OTE50795DdgYQoJKzu0+3duzc1atRgxowZGR4bN24coaGhrF27Ns8CtAXp+7BOXvcZ7dun5oUXHImMTOtPcXBQ+OyzFAYPLj6bFORHP5vRaOC33w5w9uxpACpUqEiXLj1xcSk+3T7SP2k9KbOcya8+XavvePXqVZ588slMH3vyyScJDQ3NdVCiZDKZ4MsvtTz1lJM54fr4mNi8ObFYJdz8EhMTzYULZwFo2rQlTz45oFglXCGKA6ublx0dHYmJicn0sZiYGByl7U/kQHQ0jBnjxM6dD9+SXboYmDMnCU9P28VVlHh5lSYoqAtOTo5UqVLd1uEIITJhdU23WbNmzJkzh3v37lkcf/DgAXPnzqV58+Z5FpwoGc6etaNzZxdzwlWpFCZMSGH5ckm4WTEajfz22wHu3QszH6tTp54kXCEKMatrum+88QZPPfUUXbt2JSAgAG9vbx48eMDRo0fRaDTMmTMnP+IUxdRPP2kYP96RlJS06UBeXia+/TaZDh1kOlBW4uLi2LFjE/fuhREaeomnnx5uXhNdCFF4Wf0prVmzJr/++itz5szh2LFjREdH4+HhQadOnXj55ZepVq1afsQpiqHJk7V8/fXDDdKbNDGyaFESlSrJdKCsXL9+jd27t5KcnIxW60CbNkGScIUoIqz6pBqNRiIjI6lUqRIzZ87Mr5hECaAoMG/ew/m3w4frmTw5BQeHLC4q4UwmE7///hsnT/4OgLd3Gbp2DcHd3cO2gQkhsi1bSVdRFGbOnMkPP/xAcnIyarWarl27MmnSJFmFSuRYampak3LjxkamT0+xcTSFm16fwpYt67lz5xYADRo0onVrqeEKUdRk6xP7/fffs2DBAipXrkz9+vW5ceMGW7Zswd7enmnTpuV3jKKYk1UJH8/eXvv//9nTvn1XatasbeuQhBA5kK2ku2bNGoKCgpg7d675m/UXX3zB0qVLmTRpEg7SJihEnjOZTJhMJjQaDSqVik6dgklOTsbDQ4Z0C1FUZWvK0D///MOgQYMsmrKGDh1Kamoqt27dyrfghCipEhMT2bRpLfv27SR90ThHRydJuEIUcdmq6aakpFCqVCmLY+k/p6RIX5wQeenOnVvs3LmZhIQENBoNLVr44+4uyVaI4iDvF5bMgWvXrjFy5EgaN25MQEAAU6ZMITk52ap77Ny5k9q1axMSEpJPUYq8dO2abJ7+X4qicPLk76xfv4qEhAQ8Pb3o3/9/knCFKEayPfRx3Lhxmfbdjh07Fq324dQPlUrFhg0bsh1AbGwsw4YNo0KFCsyePZvIyEimTp1KdHR0ppsqZCY5OZmpU6dSunTpbD+vKHiJibBpk4affrLnt98evvXs7WVebnJyErt3b+P69WsA1KpVl6CgTtjbax9zpRCiKMlW0m3RooVVx62xYsUKYmNjWbduHV5eXkDaJtvjxo3jxRdfxNfX97H3mD9/PhUqVKBSpUqcO3cu1zGJvKMocOaMHT/8YM+aNfbExVnWcDUahWeeKdmbGSiKwqZNa7l//y5qtZq2bTtSt24DVCppDRCiuMlW0l2+fHm+BXDgwAECAgLMCRcgODiYd999l/379z826d64cYMlS5awYsUKli5dmm9xCutERcHq1fb8+KM9589nnBNUo4aR//0vlYEDDZQpU7JruiqVCn//QA4c2E3Xrj0pXbqMrUMSQuQTm8+sDw0NpV+/fhbHtFotPj4+2dom8JNPPqFXr17UqVMnv0IU2WQywYEDan76yZ7NmzXm9ZTTOTsr9Opl4H//S6VlSyMluSKXkpLMjRsRuLmlDUisVMmHQYOGYWdXKIZZCCHyic2TbmxsLDqdLsNxnU73yC0E0+3Zs4dTp06xbdu2PI0pPzYuLs7u3rVj7lxYuNCJ69czll2zZkaGDDHQp4+Bh7/qklvG9+/fY+vWjSQlJTJ48DDc3Nz//5GSWybZlf7ZlM9o9kmZ5Ux+VQpsnnQfRVGULPu0UlJS+PTTT3nllVcsmqbzgk7nlKf3K470eti0CRYuhO3b02q5/04apUrB0KEwciQ0aKAG1EDJXkRFURROnDjBtm3bMBqNuLu7Y2+vwtPTxdahFTnyGbWelFnhYPOkq9PpiI2NzXA8Li4uy/7cZcuWYWdnR8+ePc3Xp6amYjKZiI2NxdHR0WJUtTViY5MwGk05ura4S02Fzz+3Z+lSe8LDLb8UqVQK7dun1Wp79DCaNy+IirJBoIWMXq9nz54dXLp0EYDq1X3p378fqakQFZVg4+iKDrXaDp3OST6jVpAyyxl3d6d86e6xedL19fXN0Her1+u5ceNGhr7ef7t69SrXr18nICAgw2MtWrTg448/5umnn85RTEajCYNB3pyZWbVKw4wZll9mKlUyMWqUHX36JFG+/MN9cA2Ggo6ucIqIeMD27ZuIjo5CpVIRENCWZs1a4OTkRHJygrzXckA+o9aTMrOOkk/jO22edNu1a8e3335LVFQUnp5piwDs3LkTvV5PUFDQI68bPXo0ffr0sTj23Xffce3aNaZOnUrVqlXzM+wS68GDh7Xbdu0MjBmjp2NHhdKlXYiKUiTRZuLvvy8QHR2Fi4srXbv2pHz5ijIdSIgSKsdJNzQ0lOPHjxMVFUX//v3x9vbm3r17uLu74+jomO37DBo0iB9++IGXXnqJl156iYiICD777DOeeOIJi+bld999l3Xr1nHhwgUgrYb83+bntWvXcu/ePVq1apXTlyWsMGxYKh06GGWAxmO0ahWIokDTpi1wcnK2dThCCBuyOukajUY++OAD1q5dax7s1K5dO7y9vfnoo4+oW7cur732Wrbvp9PpWLZsGVOmTOGVV17B0dGRkJAQxo0bZ3GeyWTCaDQ+4i5CFB6RkRH8+ecJgoI6Y2dnh1qtpk2bR7faCCFKDquT7rfffsumTZt4++23adu2rcVax23btmXt2rVWJV2AatWqsWjRoizP+eyzz/jss88ee44QtnTp0l/s27cLgyEVV1c3WrTIOOZACFFyWZ10165dy0svvcSIESMy1DwrVaokW/2JEslgMHDo0F4uXDgLQMWKlalf38/GUQkhChurk+69e/do3Lhxpo85ODiQkCDTH0TJEhMTxfbtmwgPfwBA8+ataN48QFaXEkJkYHXSLVWqFDdv3sTf3z/DY9euXaNcuXJ5EpgofKKiYNcumw94L1Ru3LjG9u2bSU3V4+joROfO3fHxqWrrsIQQhZTVX8WDgoKYN28e9+7dMx9TqVTExcWxfPlyOnTokKcBisLhzBk7unRx4fDhtKSr0SjUqSNz/lxddSiKQrlyFRg4cIgkXCFEllSKYt0U4PDwcPr3709cXBytWrVi7969tGnThsuXL6PRaFi9ejUeHh75FG7BiIqSBQv+7ccf7ZkwwcG8gUGpUibmzUsmKCitT1+jscPT06XElFtqair29vbmn+/fv0epUqVRqzPupvQoJa3M8oqUm/WkzHLGy8slX6ZDWn3H0qVL8+uvv9KzZ0/Onz+PWq3m4sWLtGvXjhUrVhT5hCseSkqC1193YOxYR3PCbdbMyK5dieaEW9L8808oP/ywkNu3b5qPlSlT1qqEK4QouXLUQVe6dGkmTZqU17GIQuSff1Q8+6wT5849TCbPPqtn0qQUcrikdZFmNBo5duwwp0//AcCZMyepWLGyjaMSQhQ1MipGZLBjh5oxY5yIiUmr3To5KXzxRTL9+5fMNR7j4+PYsWMzd+/eAcDPrwkBAe1sHJUQoiiyOum+8847WT6uUqn49NNPcxyQsB2jEaZP1/Lllw+34Kte3cTixUnUq1cy+4Ju3PiHXbu2kpychFarpUOHrvj61rJ1WEKIIsrqpHvs2LEMx6Kjo0lMTESn0+Hm5pYngYmCN3u2ZcLt0SOV2bOT/7XxfMly714YmzatAaB0aW+Cg0Nwd/e0cVRCiKLM6qS7Z8+eTI8fOXKEiRMnMmvWrFwHJWzjzz8fjqt7990UXntNT0neDKdMmXL4+tbC0dGRNm3ao9FIb4wQInfybDx0QEAAQ4YM4ZNPPsmrWwobevrp1BKZcO/cuUVKSjKQ1lXSpUsPgoI6S8IVQuSJPJ2E5Ovry9mzZ/PylkIUCEVR+OOPo6xfv4q9e3eQPn1dlnIUQuSlPP36fvz4cfNG9EIUFUlJiezatZWbN68DoNU6YDKZZO6tECLPWZ1058yZk+FYamoqf//9NwcOHGDkyJF5EpgQBSEs7DY7dmwmISEejUZD27YdqVu3ga3DEkIUU3mSdLVaLRUrVuTVV1+VpCuKBEVROH36BEePHkRRFDw8PAkODqFUKW9bhyaEKMasTroXL17MjziEKFB6fQpnzpxEURRq1qxNUFAXtCVxqS0hRIGyapRIcnIyb775Jn/88Ud+xSNEgXBwcKRr1560a9eJzp17SMIVQhQIq5Kuo6Mju3fvxsqNiYSwOUVROHPmFH//fcF8rHz5ijRo0AhVSZwbJYSwCaubl+vUqcOlS5do0aJFfsQjRJ5LSUlh374dhIambT9ZvnxFdDp3W4clhCiBrJ6EOG7cOBYtWsTvv/+eH/EIkafCw+/z668/Ehp6GTs7O1q1CsTNrYSuaymEsLls1XSPHz9OvXr1cHFxYeLEiSQkJDBs2DB0Oh1lypSxOFelUrFhw4Z8CVaI7FIUhb/+OsvBg3sxGo24uroRHBxC2bLlbR2aEKIEy1bSfeaZZ/jll1/w8/PDw8NDNqoXhZqiKOzZs93cf1ulSnU6dQrG0dHJxpEJIUq6bCXdfw+cWr58eb4FI0ReUKlUuLq6oVKp8PcPpHHj5jJYSghRKMgq7qLYSE1Nxd7eHoAWLQKoXr0G3t5lbRyVEEI8JKu5iyIvNTWVPXu2s27dSoxGA5C2UYEkXCFEYZPtmu6wYcOy1USnUqk4ceJEroISIruioiLZvn0TkZHhANy+fRMfn2o2jkoIITKX7aTbsmVLvLy88jMWIaxy+fJF9u3bSWpqKk5OznTp0oNKlXxsHZYQQjxStpPumDFj8PPzy89YhMgWg8HA4cP7OX/+TwAqVKhEly49cHFxtXFkQgiRNRlIJYqcAwd2c/HieQCaNWtFixYBstm8EKJIkKQripxmzVoRFnabtm07SP+tEKJIkaQrCj2j0citWzeoUiUtwbq7e/D008OldiuEKHKylXRlD11hK3FxsezYsZl798IICeljrtlKwhVCFEVS0xWF1j//XGX37m2kpCTj4OCAySRbSgohijZJuqLQMZlMHDt2mFOnjgPg7V2W4OAQ2Y5PCFHkSdIVhUpCQjw7dmwmLOw2AA0bNqZ163ao1fJWFUIUffKXTBQqt27dICzsNvb2Wjp06EqNGrVsHZIQQuQZSbqiUKldux5xcXHUqFELDw9PW4cjhBB5SoaACptKTExg166tJCUlmY81b95KEq4QoliSmq6wmTt3brFjx2YSExMwGFLp1u1JW4ckhBD5SpKuKHCKonDy5HF+//0wiqLg6VmKli3b2DosIYTId5J0RYFKTk5i166t3LjxD5DWh9uuXSfz5vNCCFGcSdIVBSYyMpxNm9YSHx+HWq2mXbtO1KlTP1v7NAshRHEgSVcUGBcXN+zs7HB39yA4+AlKl/a2dUhCCFGgJOmKfJWaqkejsUelUuHg4EBISF+cnZ3Rah1sHZoQQhQ4mTIk8s39+3f55ZflnD9/xnzMw8NTEq4QosQqFEn32rVrjBw5ksaNGxMQEMCUKVNITk7O8pr4+Hi+/vprBgwYQPPmzfH392fkyJGcP3++gKIWj6IoCmfPnmbNml+IjY3h7NlTGI1GW4clhBA2Z/OkGxsby7Bhw0hISGD27NmMHz+ejRs38v7772d53Z07d/jll19o3bo1X375JVOnTsVkMjFo0CBJvDak16ewY8dmDh7cg8lkpFo1X/r2HYRarbZ1aEIIYXM279NdsWIFsbGxrFu3Di8vLwDUajXjxo3jxRdfxNfXN9PrKlWqxM6dO3FycjIfa926NZ06deKHH35g6tSpBRK/eOjBg/ts2bKBmJho7OzsCAhoh59fExmdLIQQ/8/mNd0DBw4QEBBgTrgAwcHBaLVa9u/f/8jrnJ2dLRIugIODA76+vty/fz/f4hWZS0xM5NdffyYmJhpXVzd69x5Io0ZNJeEKIcS/2DzphoaGZqjNarVafHx8CA0NtepeiYmJ/PXXX1SvXj0vQxTZ4OzsTIsW/vj4VGPgwCGUK1fB1iEJIUShY/Pm5djYWHQ6XYbjOp2OmJgYq+711VdfkZSUxJAhQ3IVk1pt8+8iNmFn97BWqlbbodEoWZ4fERGOnZ0dpUuXBqBlS3+aNTNJ7TYb0t9jJfW9llNSbtaTMsuZ/PozZvOk+yiKolj1x3vjxo0sW7aMDz/8kCpVquTquXU6p8efVAz9eyVGDw9nPLPY6OfPP/9k8+bNeHh4MHr0aADc3Z3zOcLip6S+13JLys16UmaFg82Trk6nIzY2NsPxuLi4Rw6i+q/Dhw/zzjvvMHLkSAYPHpzrmGJjkzAaTbm+T1GTmupA+lsiOjoRR8eMNV2DIZX9+/dw/vxZABwdnYmMjKNsWa8SW245oVbbodM5SZlZScrNelJmOePu7oSdXd63Dtg86fr6+mbou9Xr9dy4cYN+/fo99vozZ87w8ssv061bN9566608icloNGEwlLw3p8n0MMmmlYFl0o2OjmL79o1ERIQD0KJFAM2atUKr1fzrmpJXbrkhZZYzUm7WkzKzjpJ171qO2TzptmvXjm+//ZaoqCg8/789c+fOnej1eoKCgrK8NjQ0lNGjR9O0aVOmTp0qfYn56MqVv9m7dyepqXqcnJzp3Lk7lSvnrhlfCCFKGpv3rA8aNAg3NzdeeuklDh48yLp165g8eTJPPPGERfPyu+++S7169cw/R0REMHLkSOzt7Rk1ahTnz5/n9OnTnD59mgsXLtjipRRb6StMpabqqVChIgMHDpGEK4QQOWDzmq5Op2PZsmVMmTKFV155BUdHR0JCQhg3bpzFeSaTyWIpwStXrhAWFgbA8OHDLc6tWLEie/bsyffYSwqVSkWXLj34669zNGvWKl/6OYQQoiRQKUp+tVwXXVFRCSWy72P4cEe2bEkbwrx9+2lSU+/SsmWbx16n0djh6elSYsstJ6TMckbKzXpSZjnj5eWSL9OsbF7TFYWLnZ2Rzp13ceTIUQDKl68kTclCCJFHJOkKM602lhEj1lG58i0AGjVqRoUKlWwclRBCFB+SdAUA169fo379bdjbJ5Gc7ED79sE0bVrD1mEJIUSxIklXcOLEMY4dO4y9Pdy5U55VqwYweLA9IN39QgiRlyTpCtzd0+ZH37vXjEWLumE0aoB42wYlhBDFkMz9KKH0er353zVq1GLAgMFcvx78/wlXCCFEfpCkW8KYTCaOHz/CTz8tISHhYW3W27usDaMSQoiSQZJuCZKYmMimTWs5fvwIiYkJXLnyt8XjMmNbCCHyl7QllhB37txi587NJCQkoNFoaNeuM3XqPFxW89YtFQcOpL0d1GoFFxfJwEIIkdck6RZziqJw6tRxjh07jKIoeHp6ERwcgpdX6X+dA2+/7UhCQtqGEf/7XyqurraKWAghii9JusXcmTOnOHr0EAC1atUlKKgT9vZai3PWrNGwa1faW6FsWRMffphS4HEKIURJIEm3mKtXryGXLl2gfv1G1K3bIMP2hxERKt5/38H887RpKbi7F3SUQghRMkjSLWYUReHatStUq1YDlUqFvb09/fr975E7A73/vgMREWmPPfFEKj16GAoyXCGEKFFk9HIxkpKSzLZtG9m2bSOnT/9hPv6ohLt7t5rVq9N2FfLwUPj0U2lWFkKI/CQ13WLiwYN7bN++idjYGOzs7DL02/5XfDyMG+do/nnixGTKlpURy0IIkZ8k6RZxiqJw/vwZDh3ah8lkxM1NR9euIZQtWy7L6z75xIHbt9NqwO3aGRg0SJqVrWEymTAac15mJpOK5GQ1en0KRqN82ckuKTfrSZllpFZrHtkCmN8k6RZher2e/ft3cvly2iIXVav60rFjMI6Ojlle9/vvdixenNas7OysMGNGMv8ZXyUeQVEUYmMjSUrK/drU4eF2mEyyqbi1pNysJ2WWkZOTKzqdV4bBpflNkm4RFhMTRWjoZVQqFQEBbWnUqNlj30ApKfDGG44oStp548enULWqfPvNrvSE6+rqiVbrkKsPrFqtkppHDki5WU/K7CFFUdDrU4iPjwLA3b1UgT6/JN0izNu7LO3bd8Hd3YPy5Stm65ovv9Ry6ZIagCZNjDz3XGp+hlismExGc8J1ddXl+n4ajR0Gg9Q+rCXlZj0pM0tabdo0yfj4KNzcPAu0qVlGLxchqamp7Nu3k/Dw++ZjderUz3bCvXDBjtmz0wZYaTQKM2cmo1bnS6jFktFoBB5+YIUQRVf65zg3YzNyQpJuEREZGcHq1T9x4cJZduzYbHX/jNGY1qxsMKQ1h776qp769eWbb04UdB+QECLv2epzLM3LRcClS3+xb98uDIZUnJ1dCArqbHVzyMKF9pw8mVatrVnTyNix+sdcIYQQIq9J0i3EDAYDhw7t5cKFswBUquRD587dcXZ2sfpeS5akNSurVAozZ6bgIC2kQghR4CTpFlJJSYls3Lia8PAHADRv7k/z5v457vCPi0v7f6VKCq1aGfMqTFFELVo0nyVLFph/dnd3x8enKs88M4KAgMAM58fHx/PDD0vZt28P9+6F4ezsTKNGTXnmmWepU6duhvMNBgPr169m27Yt/PPPNYxGAxUqVKJr12706TMANze3fH19tjZnzlfcuXObTz/93NahFKgbN67z1VczOHPmFI6OTnTuHMyLL76Mg0PW0xhffvk5Tp8+meH4jz/+SpUqVS2OXb0ayvz5czh9+iQmkwkfn6q8/vo4GjZsBMD27Vv4/vvFfP/9L6gL4aAVSbqFlKOjE05Ozjg5OdG5cw8qV66SJ/eV7kiRzsHBgVmz5gEQEfGAH35YyvjxbzB37gLzHzCAqKhIXn75OSIjIxk6dAR169YjMjKCX3/9hRdeGMGkSZ/Rrl178/l6vZ633nqdM2dO0bt3f5599jkcHBy4cuUSa9b8yq1bN3n33Y8K+uUWmAcP7rNmzSq++WbB408uRuLi4njttRcpV64cU6ZMJyoqkjlzviQ2NoYPP5z82OsbNmzEmDGvWxwrV668xc9XrlxmzJhRBAQE8vHHn6LRqPn774skJyebz+ncOZiFC+exdesmQkJ65clry0uSdAsRo9GIoihoNBpUKhWdO3fHaDTi6lq8awXCNuzs7GjQoKH55/r1/ejTpztbt26ySLpffPEZt2/fYtGiH/D1rWE+HhTUkVdffYFPPvmIBg3W4OWVNt9x0aL5nDx5nM8/n4W/f2vz+U2bNqdPnwGcPPlwXfCClJKS/NgaV15Yv34NPj5VqFOnXq7vVVAx54X161cTFxfLkiU/4eHhAaSt/DRp0vs888yzVK1aLcvr3dzcLN6PmZkxY+r/J9xPzMdatPC3OEetVtOtW09WrVpRKJOujF4uJGJjY1i79hcOHdprPubk5CwJVxSY0qVL4+Hhyb1798zH7t69y/79e+natbtFwgXQaDSMHv0iCQkJbNy4DoCUlBTWrFlF27btLRJuOnt7e1q1Csgyjri4OL78cjp9+vSgQ4cABgx4knnz5pgf7927JzNnTrO4Zu/eXQQGNics7A4AYWF3CAxszpYtG5k2bQo9enRi1KhnWLRoPj16dMJgsJwmcvXqFQIDm3PkyCHzsd9+O8To0cPo2LENISGdmTFjKklJSVnGDrBt22bat+9ocez69X/46KN36Nu3J506tWHIkAH8/PMPFrMQHhUzpLUezJ8/l379QujQIYDBg/uzY8c2i+c4d+4M48ePpVevbnTuHMjw4f9j27bNj403rxw9+hvNm7c0J1yA9u07otVqOXLkcK7v/88/1zh37gz9+z/12HM7dOhEaOhl82p9hYnUdAuBa9dC2bNnGykpKcTERNGiRQAuLq62DkuUMImJicTGxlCx4sN536dPn0BRFIvm439r0qQZrq5unDp1gmHDRnLx4l8kJSUSENAmRzHo9Xpee+0FwsLCGDFiNL6+Nbh//x5nzpzO0f3mz59D69bt+PjjTzAajVSsWIklSxZw7NgR2rRpaz5v587tuLu7m2tNe/fu4qOP3qVHjycYOfJ5IiLCmTdvDnFxsUycOPWRz3fz5g3u3g3Dz6+xxfEHD+7j41OVLl264+zszJUrl1i0aD7JyUmMGDE6y5gBPvxwAmfO/MmIEaOpWrUqR44cZvLkD3BzczOX9d27YTRs2Ijevfuh1Tpw9uyffPbZZBRF4YknnsyynP77JSQzdnZ2WY4p+eefa/Tsafk8Wq2WChUqcf36tcfe/9Spk3TuHIjJZKJevQaMGvUCjRs3NT9+/nzagNL4+HiGD/8f166F4u1dhkGDBtO//yCLe1Wr5ourqxvHjx+jZs3aj33ugiRJ14aMRiPHjh02b8NXpkw5goNDJOEWMRs2aJg2TUt8vG06zF1dFSZM0PPEE9ZP8k//Y5ueVFxcXBkw4Gnz4w8epA3kK1u2fKbXQ1q/24MHaQu2pC/cUqZMWatjgbRa4qVLfzNv3mIaNPAzH+/ePSRH96tVqw7jx7+X4diuXdstku7u3Tto374TGo0GRVGYO3cWHTt2YcKED8zneHl58fbbYxk2bBTVq/tm+nwXL14AoHp1y1aB5s1b0rx5SyBtGUI/v8YkJyezevXKDEn3vzGfPPkHhw4dYObMObRsmfaloEULfx48eMDixfPNSbdz52DzNYqi0KhRE+7fv8f69WuyTLphYXcYMCDrpAwwYsRoRo58/pGPx8XFZtoy5+bmRmxsbJb3btKkGd269aRyZR/Cwx/w888/8PrrLzFnznfm90FkZAQAkyZ9wNNPD6F+/Tc4dGg/X301A53Ona5du5vvp1KpqFGjJhcunHvs6ypoknRtJD4+jh07NnP3blpzmJ9fUwIC2hbK0XYia3Pnarl82ba/t7lztVYn3aSkJNq3f9gfplar+eyzL6hc2cfq509faEBRFIufrXXixO9UrVrNIuHmhr9/xhp3587BLFmywNxfeuHCOe7cuU2XLt0AuHnzOnfvhvHqq29a1AAbN05b2/zvv/96ZNKNiAjHzs4Onc5ymdCUlBR++GEpO3Zs5d69uxb3TUxMxNnZ+ZEx//77UXQ6d5o2bW5xXbNmLfjyy+kYjUbUajWxsbEsXjyfgwf3Ex7+wFxLdnd3z7KMSpf2ZuHC77M8J/28x8n81648dgDnf5N569ZtGTp0IEuXLmTGjNkA5qb4kJAnGTp0BJA2TuD27Vt8//1ii6QLaa87IiLisTEXNEm6NmAymdiw4Veio6PQarV06BCMr29NW4clcujll/V89plta7pjxli/2ImDgwNz5y7AZDJx69ZN5s2bw+TJH/H9979QunRpALy90/7Q3rsXRs2atTK9z927YeZpQ97eZf///Ls5eSnExMRQqtTj/7hnl6enV4ZjnTt35dtvZ3Po0EE6derCrl3bKVOmLI0aNQEgOjoagHffHZfpPbN6bXq9HrVanaEZ9ttvv2bjxrWMGDGa2rXr4ubmxsGD+1m2bBF6vd4i6f435piYaGJjYyy+IP1bREQ4ZcqU5dNPP+bcuTMMHz6KatV8cXFxYe3aX9mzZ+cj44W0fvYaNTL/3f7b46YrurnpiEufm/gvcXHxVKmS9SCq/3JyciIgIJB9+3abj+l0aV8emjZtYXFus2YtOHr0NwwGAxrNw5Sm1TqQkpJi1fMWBEm6NmBnZ0ebNkH8/vsRunbtibu7h61DErnwxBOGHDXt2noRejs7O/MI23r1GuDjU5XnnhvG0qULGDfuHeBh7e7QoQMEBgZluMeff54iPj6OJk2aAVCnTl2cnV04evQwTzzR2+qY3N3dCQ29kuU5Dg4OpKZalvejmi8zq2GlJ9jdu3fQoUMn9u7dTadOXc218/Q/7mPHvk39+g0yXJ9VjU+n05GamkpKSgoO/1qBZu/eXfTq1ZchQ4abj/3226FM7pAxZjc3HR4ensyYMSvT8z09vUhJSeHIkcOMGfO6Rf9mestDVvKqeblq1WoZ+m71ej137tzK0NebHf+N/b/zdf99XmYtK3FxsY+t5duCJN0CkpiYQHR0FBUqVAKgSpXq+PhUk3V8RaFRp05dOncOZsuWjYwYMZpSpUpTrlw5goI6sG3bZgYOfNqir9JgMLBgwbe4uLiYE6yDgwN9+vTn55+Xc/z40QzTOQwGAydP/mHum/yv5s1bsXv3Ts6dO/vI6SPe3mUy/HE/fvyYVa+1c+euzJ49k99+O8iDB/fNTcuQ9se9TJmy3Llzm379Blp1Xx+fqkBaIvv3FJmUlBQ0Gnvzz0ajkd27d2Trni1atOSnn75Ho7GnRo3MW8Ti4+MxGo3Y2z98jsTEBA4dOvDY++dV87K/f2uWLVtETEy0uSJx4MBe9Hq91QPrkpKSOHLkEHXrPpx21bBhI9zcdPzxx+8WI+NPnDhO1arVLWq5kPY7SO9HL0wk6RaA27dvsGPHFkwmIwMGDDF/k5aEKwqb4cNHsmvXdlau/JkXX3wFgDffnMDVq6G88srzDBkygjp16hIVFcWvv67gwoVzTJr0mXmOLqT1z128eIHx49+gT5/+tGjhj1ar5dq1UNasWUX9+g0fmXSDg3uwdu0qxo9/nREjRlO9eg0ePLjP6dOnzIOLOnbszPTpn7J48Xc0bOjHb78d5q+/zlv1Ojt06MxXX81gxozPqFzZh9q165gfU6lUvPzyWCZOfI/k5CQCAgJxcnLi7t0wjhw5xHPPjcHHJ/PFaurWrY9arebvv/+ySLotWrRi48Z1VKtWHQ8PD9asWYVen71tNVu08KdNm7a8+eYrDB78DL6+NUlKSuLatavcvn2TCRM+wNXVlbp16/HDD0vx8PBArdbwww9LcXFxJTo6Msv729vb58mc4l69+rF69UomTHiT4cNHmRfH6Nq1u0VZTJ06iW3bNrN/f9oXpT//PMXPPy+nXbsOlCtXnvDwB6xY8QORkRFMnvyZRZwjRozim29m4+rqSv36DTh8+CC//XaITz+dYRFLQkI8N25c59lnH10ztxVJuvlIURROnDjG8eNHUBQFL69SVu8OJERB8vGpSufOwaxb9ytDh47A1dUVT08vvvtuGcuXL2H9+tV8991dnJ2d8fNrwrx5SzIsA6nVavnii69Zt+5Xtm3bwoYNa83TdYKCOvLUU/975PNrtVq++upbvvvuG5YvX0JsbCze3mUsRuY++WRvbt68ybp1q1m58ic6derK6NEvMmVK9le5cnf3oEWLVhw5cjjD6GFIS+xubq4sW7aYHTu2AmmjtFu1am3xBeO/nJyc8PdvzdGjvxEc3MN8fOzYt/j886l8+eXnODo60r17CO3adWDatCnZinfKlOn88MNS1qz5lXv3wnBxcaV6dV969HjCfM5HH33C9Omf8MknH6PTudO//yCSkhJZseKH7BZLrri5uTFr1rd89dXnvPfeWzg6Ov7/MpCvWJxnMpnMg7wASpUqjV6fyvz5c4iJicHR0YmGDf0YN+4d6tWzbN4fOPB/gIpVq1awdOlCKlasxHvvfZxhStvRo0dwdHQkICDjXHFbUynZafQvYaKiEnLd15aUlMiuXVu5efM6kLbvbdu2HS2af/LK1asqdu7UYMxiSeXPP3cgIUGFj4+JP/5IyNPn12js8PR0yZNyK8xSU/VERIRRqlR57O21ub6frft0i6rCXm6HDh1g4sT32bBhO05OTrYOByj8ZZbX3n33LVxdXbNcbvRxn2cvLxfU6rxfP0pquvkgLOw2O3ZsJiEhHo1GQ7t2nahTp36+PFdyMvTo4UxkpCwuJkRh0KZNWypX9mHDhjU89dRgW4dT4ty+fYujR39j+fJfbB1KpiTp5oPLly+SkBCPh4cXwcEhlCpVOt+e6+5dlVUJV3YYEiJ/qVQq3nrrHS5dKnxLEJYE4eEPGD/+PSpWrGTrUDIlSTcftG4dhIODI02btsiTZsjsatXKwHPPPXpwhqurQtu2knSFyG9169anbt38ad0SWWvUqIl5znVhJEk3D9y7F8b582do374LdnZ2aDQaWrXK2dqzuVGxopKj+aJCCCEKhiTdXFAUhbNnT/HbbwcwmUx4eZWmceNmtg5LCCFEISVJN4dSUlLYu3cHV69eBqB69ZrUrZtx9RpR/MiAfyGKPlt9jiXp5sCDB/fZvn0jsbEx2NnZ0bp1Oxo2bCKLXRRz6ZtR6PUpaLUOjzlbCFGY6fVp6zKr1QWbBiXpWuny5b/Zs2cbRqMRV1c3goNDstz2TBQfdnZqnJxciY+PAtIWVM/NFy2TSYXRKLVma0m5WU/K7CFFUdDrU4iPj8LJyfWxGznkNUm6VvLyStsBpEqV6nTqFIyjY+GY/C4Khk6X9vtPT7y5YWdnJyuU5YCUm/WkzDJycnI1f54LUqFIuteuXWPKlCmcOHECJycnevbsybhx43B0dHzstWvXrmX+/Pncvn2bKlWqMGbMGLp37/7Y66zx7x1DSpXypl+//1GqVGlpTi6BVCoV7u6lcHPzxGjM+UhxtVqFu7szMTGJUgOxgpSb9aTMMlKrNQVew01n86QbGxvLsGHDqFChArNnzyYyMpKpU6cSHR3NjBkzsrx227ZtTJgwgeeee442bdqwa9cuxo4di5ubG4GBgXkS38WL5zl0aB8hIX0oV64CkL3NnEXxZmdnh51dzudgazR2ODo6kpRkLFHL8+WWlJv1pMwKF5sn3RUrVhAbG8u6devMTbdqtZpx48bx4osv4uvr+8hrZ82aRbdu3XjzzTcB8Pf359q1a8yePTvXSTc1NZWDB/dw8WLa7iUXLpw1J10hhBAiJ2y+YO+BAwcICAgwJ1yA4OBgtFot+/fvf+R1N2/e5OrVq4SEhFgcDwkJ4cyZM0RGZr2dVVaioiJZvfpnLl48j0qlomXL1nTo0DXH9xNCCCGgENR0Q0ND6devn8UxrVaLj48PoaGhj7zu6tWrAFSvXt3iuK+vL4qicPXqVYtEnl2JiXp++mk5RmMqWq0zjRr1xMvLh2vXHn+tLdy+bfPvTUIIIbLJ5kk3NjYWnU6X4bhOpyMmJuaR16U/9t9r3d3dLR63loODmjFjXiQ1VUNCgguKUriTmrc3pH83cXHR4OXlUuAxpI8nc3d3QtaNyB4ps5yRcrOelFnO2Nnlz0BZmyfdR1EUJVujg/97TvoqIzkdWaxWq/H09MzRtban+v//bMNWowGLMimznJFys56UWeFg89+CTqcjNjY2w/G4uLhMa8DpHlWjTb9XVtcKIYQQtmDzpOvr65uh71av13Pjxo0sRy6n9+Wm9+2mCw0NRaVSZejrFUIIIWzN5km3Xbt2HD16lKiohyv87Ny5E71eT1BQ0COvq1y5MtWrV2fLli0Wxzdt2oSfn1+OBlEJIYQQ+cnmSXfQoEG4ubnx0ksvcfDgQdatW8fkyZN54oknLGq67777LvXq1bO49tVXX2Xr1q18+eWXHDt2jE8//ZTDhw/z6quvFvTLEEIIIR7L5gOpdDody5YtY8qUKbzyyis4OjoSEhLCuHHjLM4zmUwYjUaLY927dyc5OZl58+axaNEiqlSpwpdffplnq1EJIYQQeUmlyOagQgghRIGwefOyEEIIUVJI0hVCCCEKiCRdIYQQooBI0hVCCCEKiCRdIYQQooBI0hVCCCEKiCRdIYQQooCUmKR77do1Ro4cSePGjQkICGDKlCkkJydn69q1a9fSrVs3GjZsSEhICFu3bs3naAuHnJRZfHw8X3/9NQMGDKB58+b4+/szcuRIzp8/X0BR215u3mvpdu7cSe3atQkJCcmnKAuX3JRZdHQ0H3/8MYGBgTRs2JDg4GBWrFiRzxEXDjktt8TERGbMmEHnzp1p1KgRXbt25euvv0av1xdA1LZ1/fp1PvzwQ3r16kW9evWs+ozlRS6w+YpUBSE2NpZhw4ZRoUIFZs+eTWRkJFOnTiU6OpoZM2Zkee22bduYMGECzz33HG3atGHXrl2MHTsWNze3Yr3yVU7L7M6dO/zyyy/069ePV199FYPBwPfff8+gQYNYsWIF9evXL8BXUfBy815Ll5yczNSpUyldunQ+R1s45KbMEhISGDp0KA4ODrz77ruUKlWK69evk5qaWkDR205uyu3jjz82/y2rWbMmZ86cYfbs2cTExPD+++8X0CuwjcuXL7N//34aNWqEyWQiu+tD5VkuUEqA+fPnK40aNVIiIiLMxzZs2KDUqlVLuXLlSpbXduvWTXn11Vctjj377LPKgAED8iXWwiKnZZaQkKAkJiZaHEtOTlbatGmjTJgwId/iLSxy815L99VXXymDBw9Wxo8fr/Ts2TO/Qi00clNmX3zxhdK5c2clKSkpv8MsdHJabqmpqUrDhg2VWbNmWRz/6KOPlICAgHyLt7AwGo3mf1vzGcurXFAimpcPHDhAQECAxc5DwcHBaLVa9u/f/8jrbt68ydWrVzM0P4SEhHDmzBkiIyPzLWZby2mZOTs74+TkZHHMwcEBX19f7t+/n2/xFhY5Lbd0N27cYMmSJcW+tvFvuSmz1atX079/fxwdHfM7zEInp+WmKApGoxE3NzeL4zqdLtu1vqLMzs76tJeXuaBEJN3Q0NAMe/NqtVp8fHwy7OX7b+l79f53b15fX18URcmwl29xktMyy0xiYiJ//fVXidjjOLfl9sknn9CrVy/q1KmTXyEWOjkts5s3bxIeHo5Op+P555+nQYMGtGrViokTJ1rdh14U5bTc7O3t6du3L8uXL+fPP/8kISGBo0ePsnLlSgYPHpzfYRdJeZkLSkyfrk6ny3Bcp9MRExPzyOvSH/vvte7u7haPF0c5LbPMfPXVVyQlJTFkyJC8Cq/Qyk257dmzh1OnTrFt27b8Cq9QymmZhYeHAzB9+nS6devGggULuHLlCjNnziQ1NZUpU6bkW8yFQW7eax9//DEfffQRAwcONB8bOnQoL7/8cp7HWRzkZS4oEUn3URRFQaVSPfa8/56T3gSTnWuLm+yWWbqNGzeybNkyPvzwQ6pUqZKPkRVujyu3lJQUPv30U1555RWL5sKS7HFlZjKZgLTaxtSpUwEICAjAYDAwffp0XnvtNby9vQsk1sIkO5/RGTNmsG/fPiZPnky1atU4f/48s2fPRqfTyX7kWciLXFAimpd1Oh2xsbEZjsfFxWX6TTHdo77FpN8rq2uLupyW2b8dPnyYd955h5EjR5aYZqucltuyZcuws7OjZ8+exMbGEhsbS2pqKiaTidjY2GI9lSOnZebh4QGAv7+/xXF/f39MJpPV3SBFTU7L7dKlSyxevJiJEycycOBAWrRowfDhw3nttdeYP38+ERER+Rl2kZSXuaBEJF1fX98MH0C9Xs+NGzcy9In8W3r7/X/b60NDQ1GpVMW6jzKnZZbuzJkzvPzyy3Tr1o233norv8IsdHJablevXuX69esEBATQokULWrRowaZNmwgNDaVFixasXr06v0O3mZyWWeXKlbG3t89wPL32kZMBM0VJTsvtypUrANStW9fieN26dTEYDNy+fTvvgy3i8jIXFO935f9r164dR48eJSoqynxs586d6PV6goKCHnld5cqVqV69Olu2bLE4vmnTJvz8/Ip1M2BOywzS3oijR4+madOmTJ06tUQ1w+e03EaPHs33339v8V9gYCAVK1bk+++/p2PHjgURvk3ktMy0Wi1t2rThyJEjFsePHDmCRqOhRo0a+RZzYZDTcqtYsSJAhgVrzp07B0ClSpXyIdqiLU9zgVUTjIqomJgYpW3btsqgQYOUAwcOKGvXrlVatWqlvPnmmxbnvfPOO0rdunUtjm3ZskWpXbu2MnPmTOXo0aPKJ598otSuXVs5ePBgQb6EApfTMgsPD1eCgoKUNm3aKL/99pty6tQp83/nz58v6JdR4HLzXvuvkjJPNzdl9ueffyr169dX3nrrLeXgwYPKkiVLlEaNGimffPJJQb4Em8hpuRkMBqV///5KQECA8tNPPylHjhxRvvvuO6Vx48bK66+/XtAvo8AlJiYqW7duVbZu3aoMGTJECQoKMv+cPuc5P3NBiRhIpdPpWLZsGVOmTOGVV17B0dGRkJAQxo0bZ3GeyWTCaDRaHOvevTvJycnMmzePRYsWUaVKFb788stivRoV5LzMrly5QlhYGADDhw+3OLdixYrs2bMn32O3pdy810qq3JSZn58f8+fP54svvuCFF17Aw8ODIUOG8NprrxXkS7CJnJabWq1m3rx5zJo1iwULFhAeHk758uUZMmQIL7zwQkG/jAIXERGR4f2R/vP3339Pq1at8jUXqBSlBMyGFkIIIQqBEtGnK4QQQhQGknSFEEKIAiJJVwghhCggknSFEEKIAiJJVwghhCggknSFEEKIAiJJVwghhCggknRFsbdmzRpq166d6X/Tpk3L9n1u3bpF7dq1WbNmTT5Gm/lzpv9Xp04dWrVqxejRozl16lS+POfQoUMZOnSo+eekpCS+/vprjh07luHc9LK9detWvsTyKMeOHbMol7p16+Lv788LL7zA2bNnc3zfH3/8sUB/v6LkKRErUgkBMHXq1AwLk5cpU8ZG0Vhn6NChhISEYDQauXLlCnPmzOGZZ57hl19+oV69enn6XB999JHFz0lJScyZM4eXX36ZVq1aWTzWvn17fvnlF5uV4xtvvEGrVq0wGAxcuHCBuXPnMnToUNatW0fVqlWtvt/PP/+Mp6cnffv2zftghUCSrihBatasScOGDW0dRo6UL1+exo0bA9CsWTN8fHwYPnw4P/30U55v1m7NRgFeXl423fijSpUq5nJp3rw5Op2O8ePHs2HDBtkXVhRK0rwsSrzr16/zzjvv0LVrVxo1akTbtm154YUX+Pvvvx97bWRkJB988AFBQUE0aNAAf39/Bg0axG+//WZx3m+//cawYcNo2rQpjRo1YtCgQRl2x7FGeqK5c+eO+divv/7Kk08+ScOGDWnZsiVjxozJsPXbzZs3GTt2LIGBgTRo0IDWrVszbNgw/vrrL/M5/25evnXrFgEBAQDMmTPH3Jw7YcIEIGPz8ieffELjxo2Jj4/PEPPrr79O69atSU1NNR/bsmULTz31FI0bN6ZJkyaMHDmSCxcu5LhcGjRoAEB4eLjF8Tlz5jBgwABatmxJ06ZN6dOnD6tWreLfq+B27NiRy5cv8/vvv5tf5793d4qPj2fatGl07NiRBg0a0LZtWz755BMSExNzHK8oeaSmK0oMk8mEwWCwOKbRaLh//z4eHh68+eabeHl5ERMTw9q1axk4cCBr167Ncq/Mt956iwsXLjB27FiqVq1KbGwsFy5cIDo62nzO+vXrGT9+PJ06dWLatGloNBp++eUXRo4cyaJFi8xJzRrXr18HwNPTE4D58+czc+ZMQkJCePPNN4mKimLOnDk89dRT/Prrr+am1tGjR2MymXjrrbeoUKECUVFRnDp1KtPN0CGt+X3hwoWMGjWK/v37M2DAAIBH1m779evH999/z9atW83nQtpm37t372bw4MHmPXDnzZvHV199Rd++fXnxxRdJTU1l0aJFDB48mFWrVuVoa7705F+tWjWL47dv3+app56iQoUKAJw+fZopU6Zw7949Xn75ZSAtMb/66qu4ubmZm9i1Wi2Q1sQ+ZMgQ7t69ywsvvEDt2rW5fPkys2fP5tKlSyxdurREbWEpciFvNksSovBavXq1UqtWrUz/S01NzXC+wWBQ9Hq90rVrV+XTTz81H79586ZSq1YtZfXq1eZjjRs3znIbucTERKVly5bK888/b3HcaDQqTz75pNK/f/8sY09/zu+++05JTU1VUlJSlHPnzin9+vVTatWqpezbt0+JiYlR/Pz8lNGjR1tce+fOHaVBgwbKG2+8oSiKokRGRiq1atVSli5dmuVzDhkyRBkyZIj554iICKVWrVrK7NmzM5ybXrY3b940H+vTp4/y1FNPWZz3448/KrVq1VL+/vtvc2z16tVTJk+ebHFefHy80qZNG+W1117LMsajR48qtWrVUjZv3qykpqYqSUlJyokTJ5Tg4GClR48eSkxMzCOvNRqNSmpqqjJnzhylZcuWislkMj/Ws2dPi9eebv78+UqdOnWUM2fOWBzftm2b+fcgRHZITVeUGNOmTcPX19fimEajwWAwsHDhQjZs2MCNGzcsmj//2zz7X35+fqxduxYPDw9at25N/fr1zTU5gFOnThEdHU2fPn0y1LLbtm3LwoULSUxMxNnZOcvnmTFjBjNmzDD/XLp0aSZNmkRQUBD79+8nOTmZPn36WFxTvnx5/P39OXr0KAAeHh74+PiwaNEiTCYTrVq1ok6dOtjZ5W0vU9++fZk8eTJXr141txKsWbOGhg0bUqtWLQAOHTqEwWCgV69eFuXi4OBAixYtMh0pnZmxY8da/Ozt7c2KFSvQ6XQWx48cOcL8+fM5e/ZshqbviIgISpcuneXz7N27l5o1a1K3bl2LeAMDA1GpVPz+++9ZbhwvRDpJuqLE8PX1zXQg1WeffcaPP/7I6NGjadGiBe7u7qhUKt5//31SUlKyvOeXX37Jt99+y6+//sqsWbNwdnamS5cuvPXWW3h7e5v7FrMa1BMTE/PYpPvMM8/w5JNPYmdnh06no1KlSubmzPSmbG9v7wzXlSlTxty/rFKpWLp0KXPnzmXhwoV89tlneHh48MQTT/D666/j6uqaZQzZ9cQTTzBt2jTWrl3Lm2++yZUrVzh79qzFqOj0cunfv3+m98juF4Fx48bh7+9PcnIyhw4d4rvvvmPMmDGsWrXK3DR85swZRo4cScuWLZk8eTLlypXD3t6eXbt2MW/ePJKTkx/7PBEREVy/fp369etn+nhUVFS24hVCkq4o8TZs2EDv3r154403LI5HRUVlqDH9l5eXF++99x7vvfced+7cYc+ePXzxxRdERESwaNEic5/rBx98QKNGjTK9R6lSpR4bY7ly5R458trDwwOABw8eZHjs/v375hgAKlasyKeffgrAtWvX2Lp1K3PmzEGv1zNp0qTHxpEd7u7udOrUiXXr1vH666+zevVqHBwcCAkJMZ+THtPs2bPN/aw5UblyZXO5tGjRAkdHR7766iuWL1/OyJEjAdi8eTMajYb58+fj4OBgvnbXrl3Zfh5PT08cHBzMZZfZ40JkhyRdUeKpVCqLJmGAffv2ce/ePapUqZLt+1SoUIEhQ4Zw5MgRTp48CUDTpk3R6XRcuXKFIUOG5Gnc6Zo0aYKjoyMbNmyge/fu5uN3797l6NGjBAcHZ3pdtWrVeOmll9ixY0eWI4bTa4zZqRGm69u3L1u3bmX//v1s3LiRLl26WHyBCQwMRKPRcOPGjUfGlxOjRo1i7dq1fPfddzz11FO4urqiUqlQq9UWtefk5GQ2bNiQ4XqtVpvp62zfvj3z58/Hw8ODypUr51m8ouSRpCtKvPbt25tHKdeuXZvz58+zaNEiypUrl+V1cXFxPPPMM4SEhFC9enVcXFw4e/YsBw8epEuXLgC4uLjw/vvvM2HCBGJiYggODqZUqVJERkZy8eJFIiMjmThxYq7i1+l0vPTSS8ycOZO3336bnj17Eh0dzdy5c3FwcDCPzr148SKTJ0+mW7duVKlSBXt7e44ePcrff//Nc88998j7u7q6UrFiRXbv3k1AQADu7u54enpSqVKlR14TGBhIuXLlmDhxIg8ePMiw2ESlSpV49dVX+eqrr7h58ybt2rVDp9MRHh7O2bNncXJyytE8W3t7e8aOHcvrr7/O999/z0svvURQUBBLlizhzTff5KmnniI6OppFixaZv0z8W61atdi8eTNbtmyhUqVKODg4ULt2bYYNG8aOHTsYMmQIw4cPp3bt2phMJsLCwjh06BDPPvvsI1syhPg3SbqixHvvvffQaDR89913JCYmUq9ePb7++mtmzZqV5XUODg74+fmxfv16bt++jcFgoHz58owePZpRo0aZz+vVqxcVKlRg4cKFfPTRRyQkJODl5UXdunUzDH7Kqeeffx4vLy+WL1/Oli1bcHR0pGXLlrzxxhvm6ULe3t74+Pjw008/cffuXSCteXb8+PEWyz5m5pNPPmH69Om8+OKL6PV6+vTpw2efffbI8+3s7Ojduzfz5s2jfPnymU6Lev755/H19eX7779n8+bN6PV6vL29adCgAU8//XSOy6J79+4sWbKEpUuXMnToUAICAvj0009ZsGABL7zwAmXLlmXgwIHmroF/e+WVV3jw4AHvv/8+CQkJVKxYkT179uDs7MyPP/7Id999xy+//MKtW7dwdHSkfPnytG7dmooVK+Y4XlGyqBTlX7PDhRBCCJFvZEUqIYQQooBI0hVCCCEKiCRdIYQQooBI0hVCCCEKiCRdIYQQooBI0hVCCCEKiCRdIYQQooBI0hVCCCEKiCRdIYQQooBI0hVCCCEKiCRdIYQQooBI0hVCCCEKyP8ByFkD6pRveS8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfhigh = df[np.isnan(df['Highest BOx Val']) == False][['Highest BOx Val', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfhigh['Highest BOx Val']\n",
    "y_true = dfhigh['ROSC']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROSC, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a6da513-a652-4ac4-9354-844511a9705c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfhigh = df[np.isnan(df['Highest BOx Val']) == False][['Highest BOx Val', 'ROSC', 'CPC12']].copy()\n",
    "\n",
    "# Do for relationship to survival as well\n",
    "# Extract the predicted probabilities and the true labels\n",
    "y_pred = dfhigh['Highest BOx Val']\n",
    "y_true = dfhigh['CPC12']\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "\n",
    "# Compute the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line (random classifier)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('CPC 1-2, ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f1cf85-346a-48c6-a6b3-293c1868a41c",
   "metadata": {},
   "source": [
    "# Figure with all raw data for Fred Chapman"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08297244-5e30-4828-91b7-fd902a64f450",
   "metadata": {},
   "source": [
    "in Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2be1f5d4-23e4-4997-8396-85a1685a0b67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "928a4368-82ad-443f-b76b-c0d86840a7bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1911b0e-5873-49a8-b1d6-43771230a5dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39047007-322f-438c-82aa-2a5994654aec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "799f1915-cf2b-4245-882d-089701fc3023",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83f836ea-756c-4ffb-9828-1d150a0e1755",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
